Powder and SLM additively manufactured parts of X5CrNiCuNb17-4 maraging steel were systematically investigated by electron microscopy to understand the relationship between the properties of the powder grains and the microstructure of the printed parts. We prove that satellites, irregularities and superficial oxidation of powder particles can be transformed into an advantage through the formation of nanoscale (AlMnSiTiCr)-oxides in the matrix during the printing process. The nano-oxides showed extensive stability in terms of size, spherical morphology, chemical composition and crystallographic disorder upon in situ heating up to 950°C in the scanning transmission electron microscope. Their presence thus indicates a potential for oxide-dispersive strengthening of this steel, which may be beneficial for creep resistance at elevated temperatures. The nucleation of copper clusters and their evolution into nanoparticles as well as the precipitation of Ni and Cr particles upon in situ heating have as well been systematically documented.

Sustainability is the core concern of every business; The exploration of avenues to maintain sustainability while staying competitive with high level of productivity remains a vital endeavor. Production flexibility is a key area that can enhance the sustainability of manufacturing industries as it can ensure product availability, scalability, agility/fault tolerance as well as disaster recovery potentials. Technological advancements have provided avenues where companies can enhance virtually all aspects of their operations for efficiency, effectiveness, and productivity. This paper uses both quantitative and qualitative research approach to identify the capability requirements for smart and effective production management and subsequent analysis is done using Multi-Criteria Decision-Making methodology to identify and rank various industry 4.0 technologies and concepts that can provide these smart capabilities in in manufacturing industries to aid the businesses to achieve sustainability with production flexibility. The paper identifies over 12 smart capabilities and 9 Industry 4.0 Technologies which are applicable to production management. It also compares results from the analytics of historical I4.0 implementation as discussed in literatures with the current state as deduces from survey feedbacks from various manufacturing industries.

Advanced ceramics are recognized as key enabling materials possessing combinations of properties not achievable in other material classes. They are characterized by very high thermal, chemical and mechanical resistance and also usually have a lower density than metals. These properties predestine ceramics for many different applications, especially space applications.In the aerospace sector aerospike nozzles promise performance and application advantages compared to classic bell nozzles but are also inherently more complex to manufacture due to their shape. AM methods drastically simplify or even enable the fabrication of those complex structures while minimising the number of individual parts. The applicability of ceramic AM (“CerAMfacturing”) on rocket engines and especially nozzles is consequently investigated in the frame of the “MACARONIS” project, a cooperation of the Institute of Aerospace Engineering at Technische Universität Dresden and the Fraunhofer Institute for Ceramic Technologies and Systems (IKTS) in Dresden. The goal is to develop novel large size aerospike thrust nozzles including areas of highest resolution and fineness. Finding a suitable AM process that enables the realisation of both aspects is extremely challenging. One possibility could be the hybridization of shaping methods, in that case CerAM VPP (ceramic additive manufacturing via vat photopolymerization) and CerAM FFF (ceramic additive manufacturing via fused filament fabrication) in combination with sinter joining. This contribution focuses on the high resolution CerAM VPP process, in particular the development, characterization and testing of a new photoreactive Al₂O₃ suspension validated by AM of novel aerospike nozzles.

Cladding is typically used to protect components from wear and corrosion while also improving the aesthetic value and reliability of the substrate. The cladding process induces significant residual stresses due to the temperature difference between the substrate and the clad layer. However, these residual stresses could be effectively utilized by modifying processes and geometrical parameters. This paper introduces a novel methodology for using the weld-cladding process as a cost-effective alternative to various existing reinforcement techniques. The numerical analyses are performed to maximize the reinforcement of a cylindrical tool. The investigation of how the weld cladding develops compressive stresses on the specimen in response to a change in the weld beads and the welding sequence is presented. For the benchmark shape, experimental verification of the numerical model is performed. The impact of the distance between the weld beads and the effect of the tool diameter is numerically investigated. Furthermore, the variation in compressive stresses due to temperature fluctuations during the extrusion process has been evaluated. The results showed that adequate compressive stresses are generated on the welded parts through the cladding process after cooling. Hence, the targeted reinforcement of the substrate can be achieved by optimizing the welding sequence and process parameters.

Background: Shared value creation in Base of the Pyramid (BoP) communities is a crucial process towards building sustainable societies. BoP communities in developing countries represent more than four billion people who live on low incomes with limited access to basic products and services. Current or emerging technologies offer promising solutions for organisations pursuing manufacturing opportunities in BoP communities. This study seeks to explore the literature on how BoP communities may become active participants in sustainably manufacturing products using micro-manufacturing factories. The research question posed is: What are the core concepts that need to be taken into consideration for creating shared value through micro-manufacturing factories in BoP communities? Method: A Systematised Literature review (SLR) was completed following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) method for data selection criteria and analysis. The SLR is used to explore the state of literature with regards to creating manufacturing shared value in BoP communities with the objective to identify study gaps and to explore shared value creation concepts. Results: Literature indicates BoP initiatives that have pursued to engage BoP communities through various innovation strategies. The findings of the review is organised under three strategic pillars: Capability building strategy, Implementation process, and Growth strategy. The capability building strategy defines the users’ intention to create shared value in BoP communities with Micro-manufacturing factories (MMF). It is followed by the implementation process which guides the users to create manufacturing shared value in BoP communities. This is followed by a growth strategy to scale for impact.

Artificial Intelligence (AI) models are expected to have a great impact in the manufacturing industry, optimizing time and resource cost by enabling applications such as predictive maintenance (PM) of production machines. A necessary condition for this is the availability of high quality data collected as close as possible to the process in question. With the advent of 5G equipped multi sensor platforms (MSPs), high sampling rate data can be collected and transmitted for processing in real time. This poses a data security challenge, since this data may give valuable insight into confidential business information of companies. Federated learning (FL) enables the training of AI models with data from multiple sources without it leaving the shop floor, by utilizing distributed computing resources available on premise. This paper introduces an architecture of FL based on data collected from 5G MSPs for enabling PM in industrial environments and discusses its potential benefits and challenges.

This research work describes an architecture for building a system that guide a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. Such media news aims to provide additional context to demand forecasts and enhance judgment on decision-making.

A Chinese superalloy GH4099 (~20 vol.% γ' phase), which can operate for long periods of time at temperatures of 1173-1273 K, was fabricated by electron beam melting (EBM). Argon gas atomized (AA) and plasma rotation electrode process (PREP) powders with the similar composition and size distribution were used as raw materials for comparison. Microstructure and mechanical properties of both the as-EBMed and post-treated alloy samples were investigated. The results show that the different powder characteristics resulted in the different build temperatures for AA and PREP samples, which were 1253 K and 1373 K, respectively. With increasing the building temperature, the EBM processing window shifted towards the higher scanning speed direction. Furthermore, intergranular cracking was observed for the as-fabricated PREP sample as a result of local enrichment of Si at grain boundaries. The cracks were completely eliminated by hot isostatic pressing (HIPing) and did not re-open during subsequent solution treatment and aging (STA). Fine spherical γ' phase precipitated uniformly after STA. The tensile strength of the HIP+STA samples was ~920 MPa in the building direction and ~850 MPa in the horizontal direction, comparable with that of the wrought alloy.

The paper is focused on the examination of the internal quality of joints created in a multi-material - additive manufacturing process. The main part of the work focuses on experimental production and non-destructive testing of restrained joints of modified PLA (polylactic acid) and ABS (Acrylonitrile butadiene styrene) 3Dprinted on RepRap 3D device that works on the "open source" principle. The article presents the outcomes of non-destructive materials test in the form of the data from the Laser Amplified Ultrasonography, microscopic observations of the joints area and tensile tests of the specially designed samples. The samples with designed joints were additively manufactured of two materials: specially blended PLA (Market name – PLA Tough) and conventionally made ABS. The tests are mainly focused on the determination of the quality of material connection in the joints area. Based on the results obtained, the samples made of two materials were compared in the end to establish which produced material joint is stronger and have a lower amount of defects.

Focusing on service control factors, rapid changes in manufacturing environments, the difficulty of resource allocation evaluation, resource optimization for 3D printing services (3DPSs) in cloud manufacturing environments and so on, an indicator evaluation framework is proposed for the cloud 3D printing (C3DP) order task execution process based on a Pareto optimal set algorithm that is optimized and evaluated for remotely distributed 3D printing equipment resources. Combined with the multi-objective method of data normalization, an optimization model for C3DP order execution based on the Pareto optimal set algorithm is constructed with these agents' dynamic autonomy and distributed processing. This model can perform functions such as automatic matching and optimization of candidate services, and it is dynamic and reliable in the C3DP order task execution process based on the Pareto optimal set algorithm. Finally, a case study is designed to test the applicability and effectiveness of the C3DP order task execution process based on the analytic hierarchy process and technique for order of preference by similarity to ideal solution (AHP-TOPSIS) optimal set algorithm and the Baldwin effect.

The paper presents the conceptualization and realization of a novel platform for additive manu-facturing, an industrial robot arm-based system for additive manufacturing applications. Tradi-tional 3D printers, especially those employing fused deposition modelling (FDM) processes, are restricted to depositing material in a single toolpath plane (e.g. x-y plane). The focus of this study was to explore the feasibility of integrating commercial off the shelf (COTS) additive manufac-turing technologies with a six degree of freedom industrial robot arm to yield a 3D additive manufacturing system with the capability to perform free-form six degree of freedom fused deposition modelling. The present paper presents the development of a platform from stage 0, i.e. materials to its use, and finally a printed product with the developed extruder.

As the additive manufacturing industry grows, it is compounding the global plastic waste problem. Distributed recycling and additive manufacturing (DRAM) offers an economic solution to this challenge, but it has been relegated to either small-volume 3D printers (limiting waste recycling throughput) or expensive industrial machines (limiting accessibility and lateral scaling). To overcome these challenges, this paper provides proof-of-concept for a novel open-source hybrid 3D printer that combines a low-cost hanging printer design with a compression screw-based end-effector that allows direct extrusion of recycled plastic waste in large expandable printing volumes. Mechanical testing of the resultant prints from 100% waste plastic, however, showed that combining challenges of non-uniform feedstocks and a heavy printhead for a hangprinter reduced the strength of the parts compared to fused filament fabrication. The preliminary results are technologically promising, however, and provide opportunities to improve on the open source design to help process the volumes of waste plastic needed for DRAM to address the negative environmental impacts of global plastic use.

The parts obtained by FFF technology are anisotropic in any direction; while the whole part constitutes out of layers, each of the layers include one or more perimeters of shell and the infill with density from 0 to 100%. Mechanical performance of FFF parts depends on its shape, orientation, constitution and 3D printing parameters. A classification for shapes of parts obtained by FFF and stressed during its operation is provided. For the most questionable class (parts with interrupted shell critically stressed on interlayer boundaries) a representative sample and testing apparatus is developed. Different approaches to the sample shape modification were applied and tested. The part shape optimization resulted in increment of the force, required to fracture the part from 483 to 1096 N.

With increasing constraints on energy and resource markets and the non-decreasing trend in energy demand, the need for relevant clean energy generation and storage solutions is growing and is gradually reaching the individual home. But small-scale energy storage is still an expensive investment in 2022 and the risk/reward ratio is not yet attractive enough for individual homeowners. One solution is for homeowners not to store excess clean energy individually but to produce hydrogen for mutual use. In this paper a collective production of hydrogen for a daily filling of a bus is considered. Following our previous work on the subject, the investigation consists of finding an optimal buy/sell rule to the grid, and the use of the energy with an additional objective: mobility. The dominant technique in the energy community is reinforcement learning, which is however difficult to use when the learning data is limited as in our study. We chose a less data-intensive and yet technically well-documented approach. Our results show that rulebooks, different but more interesting than the usual robust rule, exist and can be cost-effective. But they require fine-tuning as to not deteriorate system performance. In some cases, it is worth missing the H2 production requirement in exchange for higher economic performance.

For orthopaedic applications, additive manufactured (AM) porous scaffolds made of absorbable metals like magnesium, zinc or iron are of particular interest. They do not only offer the potential to design and fabricate bio-mimetic or rather bone equivalent mechanical properties, they also do not need to be removed in further surgery. Located in a physiological environment, scaffolds made of absorbable metals show a decreasing Young’s modulus over time, due to product dissolution. For WE43 scaffolds, during the first days an increase of the smeared Young's modulus can be observed, which is mainly attributed to a forming substrate layer of degradation products on the struts surfaces. In this study the influence of degradation products on the stiffness properties of metallic scaffolds is investigated. For this, analytical calculations and finite element simulations are performed to study the influence of the substrate layer thickness and Young's modulus for single struts and for a new scaffold geometry with adapted polar f2cc,z unit cells. The finite element model is further validated by compression tests on AM scaffolds made from Zn1Mg. The results show, that even low thicknesses and Young's moduli of the substrate layer increases significantly the smeared Young's modulus under axial compression.

Background: Drought, N deficiency and herbivory are considered the most important stressors caused by climate change in the agro- and eco-systems and varied in space and time shaping a highly dynamic and heterogeneous stressful environments. This study aims to evaluate the to-mato morpho-physiological and metabolic responses to combined abiotic and herbivory at dif-ferent within-plant spatial levels and temporal scales. Methods: Leaf-level morphological, gas exchange traits and VOC profiles were measured in to-mato plants exposed to N deficiency and drought, T. absoluta larvae and their combination. Ad-ditive, synergistic or antagonistic effects of the single stress when combined were also evaluat-ed. Morpho-physiological traits and VOC profile were also measured on leaves located at three different positions along the shoot axes. Results: The combination of the abiotic and biotic stress has been more harmful than single stress with antagonistic and synergistic but non-additive effects for the morpho-physiological and VOC tomato responses, respectively. Combined stress also determined a high within-plant phenotypic plasticity of the morpho-physiological responses. Conclusions: These results suggest that the combined stress in tomato determined a “new stress state” and a higher within-plant phenotypic plasticity which could permit an efficient use of the growth and defence resources in the heterogeneous and multiple stressful environmental condi-tions.

The move to lean construction is a lengthy process that may call into question established ways of construction management delivery. The objective of this article is to help businesses take the first step on this route by introducing a few lean manufacturing techniques and practices that may be used on projects over the next few weeks. Although the construction industry has come a long way since its start, the essential technology to change it has not yet found a home. The digital switch has now entered the construction industry to boost production. Artificial intelligence (AI) is a discipline of computer science described as the potential of machines to mimic intelligent human behavior by modelling traditionally complicated problems using human-inspired techniques. Due to its complexity, AI distinguishes itself from lower degrees of digitalization. The complexity of artificial intelligence involves the formation of new circumstances for human collaboration and trust. This dissertation investigates the application of artificial intelligence and the suitable interaction between humans and AI-based technologies. This study attempts to shed light on how the construction industry may close the gap between the potential and actual benefits of artificial intelligence deployment. Comparing the prospective benefits of AI implementation to the present benefits and challenges of AI deployment in the construction industry revealed the disparity.

Metal Additive Manufacturing (MAM) has emerged as a promising technology in the aerospace industry, enabling the production of complex components with enhanced design flexibility and reduced lead times. Concurrently, Industry 4.0 has gained significant attention for its potential to revolutionize aerospace manufacturing processes. The paper aims to cluster and review the literature on MAM in the aerospace industry, specifically focusing on its relevance to Industry 4.0 and its applicability throughout the various stages of the product life cycle for the chief purpose of matching supply with demand. Identified gaps and challenges are analyzed, highlighting the need for further research and outlining research agenda aimed at advancing the integration of MAM for Industry 4.0 in the aerospace sector. By addressing these research areas, the aerospace industry can unlock the value of MAM within an Industry 4.0 framework, leading to improved efficiency, productivity, and competitiveness.

We overview recent findings achieved in the field of model-driven development of additively manufactured porous materials for development of a new generation of bioactive implants for orthopedic applications. Porous structures produced of biocompatible titanium alloys by selective laser melting can present a promising material to design scaffolds with regulated mechanical properties and with capacity to be loaded with pharmaceutical products. Adjusting pore geometry, one could control elastic modulus and strength/fatigue properties of the engineered structures to be compatible with bone tissues, thus preventing the stress shield effect when replacing a diseased bone fragment. Adsorption of medicals by internal spaces would make it possible to emit the antibiotic and anti-tumor agents into surrounding tissues. We critically analyze the recent advances in the field featuring model design approaches, virtual testing of the designed structures, capabilities of additive printing of porous structures, biomedical issues of the engineered scaffolds and so on. A special attention is paid to highlight the current troubles in the field and the ways of their solutions.

Selective Paste Intrusion (SPI) is an additive manufacturing (AM) process in which thin layers of aggregates are selectively bonded by cement paste only where the structure is to be produced. In this way, concrete elements with complex geometries and structures can be created. Reinforcement is required to increase the flexural strength of the concrete elements and, thus, enable their applicability in practice. Integrating the reinforcement is a difficult task, particularly in the case of SPI due to the layer-wise printing method. Especially with respect to possible complex structures, the production of the reinforcement needs to be adapted to SPI, thereby offering a high degree of freedom. One concept for a reinforcement integration is combining the two additive manufacturing processes SPI and Wire and Arc Additive Manufacturing (WAAM). However, since the two processes serve different fields of application, their compatibility is not necessarily given. Ongoing investigations show that the temperatures caused by WAAM adversely affect both, the cement paste rheology required for sufficient paste penetration into the particle bed and the overall concrete strength. This paper provides an overview of ongoing research focusing on different cooling strategies and their effects on the compressive strength of SPI-printed concrete parts.

Classification predictive modeling involves the accurate assignment of observations in a dataset to target classes or categories. There is an increasing growth of real-world classification problems with severely imbalanced class distributions. In this case, minority classes have much fewer observations to learn from than those from majority classes. Despite this sparsity, a minority class is often considered the more interesting class yet developing a scientific learning algorithm suitable for the observations presents countless challenges. In this article, we suggest a novel multi-class classification algorithm specialized to handle severely imbalanced classes based on the method we refer to as SAMME.C2. It blends the flexible mechanics of the boosting techniques from SAMME algorithm, a multi-class classifier, and Ada.C2 algorithm, a cost-sensitive binary classifier designed to address highly class imbalances. Not only do we provide the resulting algorithm but we also establish scientific and statistical formulation of our proposed SAMME.C2 algorithm. Through numerical experiments examining various degrees of classifier difficulty, we demonstrate consistent superior performance of our proposed model.

Comparison of the influence of conditioning temperature of microspheres made of medical grade poly(L-lactide) (PLLA) and polylactide with 4wt.% of D-lactide content (PLA), on its thermal and structural properties is presented. The microspheres were fabricated by solid-in-oil-in-water method for application in additive manufacturing. The microspheres were annealed below glass transition temperature (Tg), above Tg but below onset of cold crystallization, and at two temperatures selected from the range of cold crystallization corresponding to crystallization of α’ and α form of poly(L-lactide) respectively, i.e., at 40°C, 70°C, 90°C and 120°C, in order to verify the influence of conditioning temperatures on sinterability of microspheres set as sintering window (SW). Based on differential scanning calorimetry measurements SW of microspheres were evaluated with consideration of existence of cold crystallization and reorganization of crystal polymorph. The results indicate that the conditioning temperature influence on availability and range of SW that depending on the D-lactide presence. We postulate the need for an individual approach for polylactide powders in determining the SW as a temperature range free of any thermal events. Moreover, other core powder characteristic, such as residual solvent content, morphology, particle size distribution, powder flowability and thermal conductivity, as a key property for successful laser sintering, are characterized. The microspheres are close to sphere and the size of microspheres are below 100 µm. Residual solvents content decreases with the increase of annealing temperature. The thermal conductivity is 0.073 W/mK and 0.064 W/mK for PLA and PLLA microspheres, respectively, and it depends on the spherical shape of the microspheres. Furthermore, the WAXD studies prove that an increase in the conditioning temperature causes a slight increase in crystallinity degree for PLLA microspheres and clear increase in crystallization for PLA microspheres.

Structural integrity is a crucial aspect of engineering components, particularly in the field of additive manufacturing (AM). Surface roughness is a vital parameter that significantly influences the structural integrity of additively manufactured parts. In this study, we present a comprehensive investigation into the relationship between surface roughness and structural integrity of Polyactic Acid (PLA) specimens produced through additive manufacturing. This research work focuses on the prediction of surface roughness of Additive Manufactured Polyactic Acid (PLA) specimens using eight different supervised machine learning regression-based algorithms. For the first time, Explainable AI techniques are employed to enhance the interpretability of the machine learning models. The eight algorithms used in this study are Support Vector Regression, Random Forest, XG Boost, Ada Boost, Catboost, Decision Tree, Extra Tree regressor, and Gradient Boosting regressor. The study analyzes the performance of these algorithms to predict the surface roughness of PLA specimens, while also investigating the impact of individual input parameters through Explainable AI methods. The experimental results indicate that the XG Boost algorithm outperforms the other algorithms with the highest coefficient of determination value of 0.9634. This value demonstrates that the XG Boost algorithm provides the most accurate predictions for surface roughness compared to other algorithms. The study also provides a comparative analysis of the performance of all the algorithms used in this study, along with insights derived from Explainable AI techniques.

Artificial intelligence (AI) and 3D printing (3DP) play critical roles in what is known as the 4th Industrial Revolution by developing data- and machine-intelligence-based integrated production technologies. This shift has led to increasingly complex design requirements and fabrication methods, posing several challenges for implementing design, fabrication process, and architectural quality standardization. This paper systematically reviews AI techniques applied in diverse stages of 3DP production in architecture. The research goals are to (1) offer a comprehensive critical analysis of the body of literature; (2) identify and categorize approaches to integrating AI in the production of 3D-printed structures; (3) identify and discuss challenges and opportunities of AI integration in architectural production of 3DP structures; and (4) identify gap and provide recommendations for future research. The findings indicate that topics related to AI applications are current and trending, receiving increasing attention from researchers in general and particularly those working in machine and deep learning, artificial neural networks, and pattern recognition. However, the potential of the application of AI in architectural production using additive manufacturing still needs to be explored. Considering this, the paper emphasizes the necessity of redefining traditional field boundaries and opening new opportunities in the sector.

To stabilize the Earth’s climate a large-scale transition is needed to non-carbon emitting renewable energy technologies like wind and solar energy. Although these renewable energy sources are now lower-cost than fossil fuels, their inherent intermittency make them unable to supply a constant load without storage. To address these challenges rechargeable electric batteries are currently the most promising option; however, their high capital costs limit current deployment velocities. To both reduce the cost as well as improve performance 3D printing technology has emerged as a promising solution. This literature review provides the state-of-the-art enhancements of battery properties with 3D printing including efficiency, mechanical stability, energy and power density, customizability and sizing, production process efficiency, material conservation and environmental sustainability as well as the progress in solid-state batteries. The principles, advantages, limitations, and recent advancements associated with the most common types of 3D printing are reviewed focusing on their contributions to the battery field. 3D printing batteries offer design flexibility, material flexibility, reduces pack weight, minimizes material waste, increases the range of applications and has the potential to reduce costs. As 3D printing technologies become more accessible, the prospect of cost-effective production for customized batteries is extremely promising.

Greenhouse gas (GHG) emissions from human activities have climbed significantly above pre-pandemic levels and reached record highs that unequivocally accelerate global warming. Industry has a significant impact on climate change, emitting at least 21 % of global GHGs and making little overall progress toward its reduction until now. Reducing industry’s emissions requires coordinated action along the value chains in order to promote mitigation options, such as energy and material efficiency, circular material flows, and transformative changes within production processes. The authors analyzed the GHG emissions generated during the manufacturing of three different products of automotive suppliers located in Austria. Despite previous efforts toward an environmentally compatible fabrication, additional and significant reduction potentials were identified. These measures for product carbon footprint (PCF) reduction included the sourcing of low-carbon materials (which are already available on the market), more extensive use of renewable energy, and changes towards more resource efficient manufacturing processes and machinery. Depending on the materials used, the PCF can be reduced by up to 80 %. The findings serve to prepare for future PCF reporting regulations and illustrate reduction potentials to achieve future market advantages, especially when PCFs become an awarding criterion.

If a lubricant contains structures capable of conducting energy, reactions involving zinc dialkyldithiophosphate (ZDDP) may take place both very close to and away from the solid surfaces, with this indicating that ZDDP can be a highly effective anti-wear (AW) additive. The central thesis of this article is that the tribocatalytic effect is observed only when the energy emitted by the solids is transmitted by ordered molecular structures present in the lubricant, e.g., graphene. The friction tests were carried out for 100Cr6 steel balls in a sliding contact with uncoated or a:C-H-coated HS6-5-2C steel discs in the presence of polyalphaolefin 8 (PAO 8) as the lubricant, which was enhanced with graphene and/or ZDDP. There is sufficient evidence of the interactions occurring between ZDDP and graphene and their effects on the tribological performance of the system. It was also found that the higher the concentration of zinc in the wear area, the lower the wear. This was probably due to the energy transfer resulting from the catalytic decomposition of ZDDP molecules. Graphene, playing the role of the catalyst, contributed to that energy transfer.

Three-dimensional (3D) and four-dimensional (4D) printing emerged as the next generation of manufacturing techniques, spanning several research areas such as engineering, chemistry, biology, computing, and materials science. Three-dimensional printing allows the manufacture of complex shapes with high precision, by adding layer by layer of different materials. The use of smart materials that change shape or color, produce an electrical current, become bioactive, or perform an intended function in response to an external stimulus. Shape memory materials (SMMs) in 3D printing technology have attracted a lot of attention due to their ability to respond to external stimuli, leading this technology towards an emerging area of research, "4D printing technology." The core part of this review summarizes the effect of the main external stimuli on 4D textile materials followed by the main applications 4D printed textiles can change their shape over time due to external stimuli such as temperature.

The development of high complexity geometry parts is one of the main goals of the additive manufacturing technology. However, the failure of printed structures and the joining of different parts to create complex assemblies represents a real challenge in the research of efficient and sustainability techniques for the permanent assembly of polymers. Laser welding processes have been used as a single step method to join metals until years ago. Nowadays, the growing trend in the use of thermoplastics for additive manufacturing has led to the need to adapt this technique to materials with a very specific nature and more sensitive to thermal effects. Also, the possibility of transmitting the laser beam through transparent polymer layers allows to focus the energy supply on internal sections of the assembled components. In this research, an infrared laser marking system was used to join two different samples of polylactic acid manufactured by fused deposited modeling technology. In order to increase the effectiveness of the bonding process, a transparent and a dark sample have been used as assembly material, focusing the laser beam on the interface area of the two parts. By means of tensile tests, dimensional measurement and the use of optical microscopy techniques, a basis was established that links the supplied energy by laser to the joining performance.

To improve accessibility, this article describes a static, four-legged, walker that can be constructed from materials and fasteners commonly available from hardware stores coupled by open-source 3-D printed joints. The designs are described in detail, shared under an open-source license, and fabricated with a low-cost open-source desktop 3-D printer and hand tools. The resulting device is loaded to failure to determine the maximum load that the design can safely support in both vertical and horizontal failure modes. The experimental results showed the average vertical failure load capacity was 3680±694.3N, equivalent to 375.3±70.8kg of applied weight with the fractured location at the wood dowel handlebars. The average horizontal load capacity was 315.6±49.4N, equivalent to 32.2±5.1kg. The maximum weight capacity of a user of 187.1±29.3kg was obtained, which indicates the open-source walker design can withstand the weight requirements of all genders with a 95% confidence interval that includes a safety factor of 1.8 when considering the lowest deviation weight capacity. The design has a cost at the bottom of the range of commercial walkers and reduces the mass compared to a commercial walker by 0.5kg (19% reduction). It can be concluded that this open-source walker design can aid accessibility in low-resource settings.

This article presents, for the first time, the efficacy and curing depth analysis of photo-thermal dual polymerization in metal (Fe) polymer composites for 3D printing of a 3-component (A/B/M) system based on the proposed mechanism of our group, in which the co initiators A and B are Irgacure-369, and charge-transfer complexes (CTC), respectively; and the monomer M is filled by Fe. Our formulas show the depth of curing (Zc) is an increasing function of the light intensity, but a decreasing function of the Fe and photoinitiator concentrations. Zc is enhanced by the additive [B] which produces extra thermal radical for polymerization under high temperature. The heat (or temperature) increase in the system has two components : (i) due to the light absorption of Fe filler, and (ii) heat released from the exothermic photopolymerization of the monomer. The heat is transported to the additive (or co-initiator) [B] to produce extra radical R' and enhance the monomer conversion function (CF). The Fe filler leads to temperature increase, but also limits the light penetration leading to lower CF and Zc, which could be overcome by the additive initiator [B] in thick polymers. Optimal Fe for maximal CF and Zc are explored theoretically.

This comprehensive review explores the advancements in Orthognathic and Oral Maxillofacial Surgery, focusing on the integration of 3D Printing and Virtual Surgical Planning (VSP). Traditional surgical methods, while effective, come with inherent risks and complications, and can lead to variability in outcomes due to the reliance on the surgeon's skill and experience. The shift towards patient-centric care necessitates personalized surgical methods, which can be achieved through advanced technology. The amalgamation of 3D printing and VSP revolutionizes surgical planning and implementation by providing tactile 3D models for visualization and planning, and accurately designed surgical guides for execution. This convergence of digital planning and physical modeling facilitates a more predictable, personalized, and precise surgical process. However, the adoption of these technologies presents challenges, including the need for extensive software training and the steep learning curve associated with computer-aided design programs. Despite these challenges, the integration of 3D printing and VSP paves the way for advanced patient care in orthognathic and oral maxillofacial surgery.

The final assembly of appliances and automotive industries is a rigorous process, that has limited capabilities of full traceability associated with: (1) the parts installed, (2) their fabrication processes, and (3) the assembly work. This is also the case for each of its sub-assemblies The many parts and sub-assemblies that compose the final assembly, make full traceability a challenging fit, that is almost unsurmountable. Such full traceability along the entire supply-chain is not existent today and must be based on documentation of most assembled parts, assembly tasks, and inspection tasks that compose the full assembled product. In addition, security measures are needed to prevent hostile hacking and unauthorized approach to the assembly documentation throughout the entire supply chain. The related documentation and repeated verifications require considerable effort and have many chances for human errors. So, automating these processes has a great value. This article expounds a framework that harnesses block-chain and smart-contract technology to offer traceable and protected documentation of the assembly process. We expand the concept of a Bill-Of-Assembly (BOA) to incorporate data from the bill of materials (BOM), the associated assembly activities, the associated activities’ specification parameters and materials, and the associated assembly resources (machines and/or operators). The paper defines the operation of the BOA with blockchain and smart-contract technology, for attaining full traceability, safety, and security, for the entire assembled product. Future research could extend the proposed approach to facilitate the usage of the BOA data structure in constructing a digital twin of the entire simulated system.

The paper contains the results of the 100-hour test campaign of the Additive Manufactured (AM) spare part (retainer – slipper) dedicated for an exact axial piston pump. The material of the retainer-slipper has been identified by using energy dispersive spectroscopy and replaced by other – with similar material properties as the original one. The obtained spare part had been subjected to only one postprocessing type (sandblasting) to analyze the influence of the rough part after the AM process. The whole test campaign has been divided into stages, where after each stage microscopic measurements have been made. During microscopical investigation roughness and geometrical measurements were conducted. The results revealed that it is possible to replace parts in hydraulic pumps with the use of AM. 100-hour test campaign caused about 500% increase in the surface roughness of the pump’s original part which was cooperated with the AM spare retainer-slipper, without any damages to the test system.

For their clinical use Mesenchymal Stromal Cells (MSCs), isolated from bone marrow (BM-MSCs) are considered Advanced Therapy Medicinal Products (ATMP) and need to be produced according to Good Manufacturing Practice (GMP). Human platelet lysate (HPL) represents a good GMP-compliant alternative to animal serum and after pathogen inactivation with Psoralen was more efficient and safer to produce MSCs in GMP. In this study MSCs cultivated in FBS (FBS-MSC) or inactivated HPL (iHPL-MSC), were compared for their immunomodulant properties. In particular, the effects of MSCs on: 1)proliferation of total Lymphocytes (Ly) and on naïve T Ly subsets induced to differentiate versus Th1 and Th2 Ly; 2) the immunophenotype of different T cell subsets; 3)the cytokine release to verify Th1, Th2 and Th17 polarization were analyzed by using in vitro co-culture system. We observed that iHPL-MSCs showed the same immunomodulant properties observed in the FBS-MSCs co-cultures. Although, a more efficient effect on the increase of naïve T cells and, in the Th1 cytokine release related to iHPL was observed. This study confirms that iHPL, used as medium supplement, may be considered a good alternative to FBS for a GMP-compliant MSC expansion to preserve their immunomodulant proprieties.

The power conversion efficiency (PCE) of polymer solar cells (PSCs) are strongly depended on the bulk-heterojunction active layer. Here, the 1,8-diiodooctane (DIO) additives have been added into the PCDTBT: PC70BM blend PSCs. Based on the higher boiling point than host solvent ODB and better solubility of PC71BM, the device photovoltaic properties with DIO additive were changed obviously. The investigations of EQE indicate that the DIO can influence the charge recombination and transportation process, and absorption studies demonstrate that the charge carriers generation process can also be affected by DIO. The overall impact reflected on the decreased equivalent resistant. With 3% v/v DIO, the Jsc, Voc and FF are both increased. Correspondingly, a highest PCE is achieved of 6.15%, while the reference device without DIO only has a PCE of 5.23%. Hence, adding DIO solvent additive is a effective method for the photovoltaic properties improving of PCDTBT: PC70BM blend PSCs.

Industry 4.0 brings advanced technologies and automation, enabling greater productivity and efficiency. However, it also increases energy consumption and carbon emissions. Sustainable manufacturing in the era of Industry 4.0 is vital for addressing the environmental impact by minimizing resource consumption, optimizing energy efficiency, and adopting eco-friendly practices. CNC (Computer Numeric Control) 5-axis milling plays a significant role in the machining of precision molds and dies, aerospace, consumer electronics, etc. This research aims to explore the potential of the artificial intelligence (AI) technique in improving energy efficiency on CNC 5-axis milling process for sustainable manufacturing. The experiments were carried out using a CNC 5-axis milling machine with various machining parameters, toolpath planning, and dry cutting condition. Data on energy consumption of CNC 5-axis milling machine were collected, then the relationship between the machining parameters, toolpath planning, and energy consumption was analyzed and built. Furthermore, an AI-based optimization algorithm was developed that uses machine learning and optimization techniques to identify energy-efficient machining strategies. The developed algorithm was implemented within a simulation framework, the algorithm was rigorously assessed using machine learning analysis to effectively reduce energy consumption, all while ensuring the accuracy of machining result. Finally, verification experiments were carried out to evaluate the applicability and performance of the AI-based energy optimization approach on a CNC 5-axis milling machine. By integrating AI into the CNC machining process, manufacturers can achieve significant energy savings, leading to reduced carbon emissions and improved sustainability.

Ledge formation presents a significant challenge in endodontic treatment. Yet, there is still a lack of educational tooth models for hands-on practice. This study aimed to create and evaluate a tooth model for ledge management practice. A natural tooth with curved roots was collected for scientific use under ethics committee approval. Following initial root canal preparation, the tooth was scanned using micro-computed tomography (μCT) and 3D reconstructed. A K-file, created via computer-aided design (CAD), was partly inserted into the root canal wall of the 3D-reconstructed tooth. By subtracting the K-file from the tooth, a tooth model with a root canal ledge was produced. The models were then 3D printed for a hands-on workshop. A seven-item Likert scale questionnaire was administered to 20 postgraduate students and 10 endodontists to assess the model's quality and training effectiveness. The feedback from both students and experts was positive, with no statistically significant differences found between the two groups (p > 0.05). This novel tooth model is expected to address the existing gap in endodontic education and provide benefits for dental practitioners in future applications.

This article explores the important role of traditional shipyards in the global maritime industry, covering aspects of construction, repair, and maintenance. With the advent of faster manufacturing techniques, traditional shipyards face important challenges such as planning errors, coordination problems, delivery delays, and underutilization of technology, which results in high costs, reduced productivity, and prolonged projects. The application of Manufacturing Cycle Efficiency (MCE) emerged as an important solution to significantly increase production efficiency. MCE empowers shipyards to deal effectively with waste, bottlenecks, and disruptions, thereby increasing performance, competitiveness, and profitability. Using a comprehensive approach that uses both qualitative and quantitative methods, including field surveys, and in-depth interviews in the traditional shipyards industry, this research identifies Non-Value-Added (NVA) processes, conducts process mapping, and calculates MCE. The findings reported in this article underscore the significant wastage in the production process, indicating an urgent need for improvement, given the current average MCE value of 67.08%, indicating considerable room for improvement. This article provides innovative perspectives on optimizing the traditional shipyards industry through production cycle efficiencies while offering actionable recommendations. Key focus areas include integrating management systems, adopting advanced technologies, and implementing sustainable strategies to improve MCE, especially by reducing non-value-added time wastage, such as inspection and storage. By implementing strategies that optimize production, minimize waste, and overcome the challenges of global competition, this research contributes to improving MCE. In conclusion, this study is an invaluable guide for industry stakeholders, enabling them to enhance their competitiveness and adapt effectively to a dynamic business environment.

Extrusion-based additive manufacturing (AM) technologies (i.e., fused filament fabrication, FFF) mostly produce thermoplastic parts. However, producing metallic or ceramic parts by FFF is also a sintered-based AM process. FFF for metallic parts can be divided into five steps: (1) raw materials selection and feedstock mixture (including palletization), (2) filament production (extrusion), (3) production of AM components using the filament extrusion process, (4) debinding, and (5) sintering. These steps are interrelated, where the parameters interact with the other ones and have a key role in the integrity and quality of the final metallic parts. FFF can produce high-accuracy and complex metallic parts, potentially revolutionizing the manufacturing industry and taking AM components to a new level. In the FFF technology for metallic materials, material compatibility, production quality, and cost-effectiveness are the challenges to overcome to make it more competitive compared to other AM technologies, like the laser processes. This review provides a comprehensive overview of the recent developments in FFF for metallic materials, including the metals and binders used, the challenges faced, potential applications, and the impact of FFF on the manufacturing (prototyping and end parts), design freedom, customization, sustainability, supply chain, among others.

A pilot study on laser 3D printing of inorganic free-form micro-optics is experimentally validated. Ultrafast laser nanolithography is employed for structuring hybrid organic-inorganic material SZ2080TM followed by high-temperature calcination post-processing. The combination allows production of 3D architectures and the heat-treatment results in converting the material to inorganic substance. The produced miniature optical elements are characterized and their optical performance demonstrated. Finally, the concept is validated for manufacturing compound optical components such as stacked lenses. This is opening for new directions and applications of laser made microoptics under harsh conditions such as high intensity radiation, temperature, acidic environment, pressure variations, which include open space, astrophotonics, and remote sensing.

We explore 3D printing modules for self-assembling spacecraft and robots – structure, actuators, electrics. Self-assembly has long been viewed as a highly desirable capability for autonomous construction of large space structures. We review self-assembly in space which focusses on the self-assembly of modules that demonstrate the range of applications. However, self-assembly may be synergised with 3D printing to offer an automated capability from raw material to modules for assembling new spacecraft or habitats. One application of 3D printing is using space debris on-orbit as an in-situ resource - defunct spacecraft may be salvaged as raw material for in-situ construction on demand. The common features of all self-assembling modules are that the modules constitute a structure housing a computer-controlled actuator internally and a reversible latching mechanism externally. We have demonstrated a 3D printed DC electric motor in which the only components that were not 3D printed are the wire coils. We have married our 3D printed motor prototype as an actuated joint between two 3D printed trigon-type panels developed as part of a trigon self-assembling system concept. The trigon concept underlies a modular approach to self-assembling and self-deploying structures. The 3D printed motorised panel system demonstrates that the motor aspect and structural aspects of robotic self-assembling machines are amenable to 3D printing. This has implications for self-assembling systems into modular satellites as a solution to space debris.

3D printing with cement-based materials is a promising manufacturing technique for civil engineering applications that already allows for the design and the construction of complex and highly customized structures using a layer-by-layer deposition approach. Usually, in this type of additive manufacturing, a mixture of cement, water, aggregate and additives is extruded through a specific print head and deposited in successive layers to form the desired shape. The extrusion mechanism is one of the most expensive parts of the 3D printer. Also, for low scale 3D printers, based on the shape of the extruder and the geometry limitation of the mixing blade, the 3D mixture is often limited to a narrow range of materials due to the risk of layer splitting or blockage. Therefore, there is a need to develop affordable and feasible alternatives to the current design-fabrication-application approach of 3D printers. In this paper, various new designed mixtures of fiber reinforced mortars that can be 3D printed using only a commercially available screw pump are analyzed based on their fresh properties and mechanical characteristics. The reduced facility requirements in this approach allow 3D-printing to be more available for civil engineering applications. With further innovation in the future, this method and mixtures can be extended for sustainable and economically feasible printing of single-family housing units.

At present, countries around the world adopt digital strategy, so that enterprises apply digital technology, digital supply chain, blockchain, and a series of digital measures to enhance their competitiveness. By analyzing the relationship between digital transformation of manufacturing export enterprises, and their business performance and export performance, this paper focused on the current situation and existing problems of digital transformation of manufacturing export enterprises in China. Based on the Resource Based View theory and Trade theory, the impact of digital transformation of enterprises performance, proposing hypotheses, and building regression models were explored. With the samples of 1007 listed companies in Shanghai and Shenzhen Stock markets from 2012 to 2019, the regression analysis, intermediate effect test, robustness test, stage lag, and heterogeneity analysis were conducted. The results shew that: (1) Listed manufacturing export enterprises carried out digital transformation, which had a significantly positive effect on enterprise performance; (2) It was found that digital transformation could improve enterprise performance by reducing costs, improving R&D intensity and improving human resources etc. (3) According to the fractal analysis, non-state-owned enterprises had more positive effects on enterprise performance, and digital transformation of manufacturing export enterprises in developed areas had a more significant impact on enterprise performance. Finally, according to the empirical results, relevant suggestions for digital transformation were provided.

A chairside polishing kit is an alternative to a laboratory polishing technique. This study aimed to evaluate the effects of different polishing techniques on the surface roughness of three-dimensional (3D)-printed acrylic denture bases (ADB). One hundred twenty disc-shaped specimens were fabricated from one conventional heat-polymerized (HP) ADB resin and two 3D-printed (Asiga (AS) and NextDent (ND)) ADB resins (n=40 per ADB resin). Each group was furtherly divided based on the polishing protocol (n=10) as follows: convectional polishing protocol (C), Microdont chairside polishing kit (M), Shofu chairside polishing kit (S), and unpolished group (U). The surface roughness (surface roughness average (Ra) and average maximum profile height (Rz)) of the printed specimens were measured using an optical profilometer, and the scanning electron microscope (SEM) was used to capture the surface at ×1000. Two-way ANOVA and post hoc tests were used for data analysis (α = 0.05) at significant levels. In unpolished groups, there was a statically significant difference between HP vs 3D-printed ADBs (p < 0.0001). For Ra values, the lowest values were presented in HP-C, AS-S, and ND-C. While the highest values were shown in all unpolished groups. Within the material, there were statistically significant differences between the three polishing protocols (C, M, and S) vs unpolished (p < 0.0001), while there was no significant between C, M, and S groups (p = 0.05). The Rz values had the same pattern as the Ra values. The two chairside polishing kits were comparable to conventional polishing technique, and it could be recommended for the clinical application.

The paper provides a comprehensive review of the recent advancements and methodologies in Human-Robot Collaboration (HRC) applied to the manufacturing assembly process. In modern manufacturing, the assembly process involves intricate and time-consuming operations, often necessitating flexible manual interventions. However, the cost and stability issues associated with manual labor highlight the need for collaborative solutions integrating humans and robots. HRC, as a viable solution, involves the joint effort of humans and robots in manufacturing tasks, presenting advantages in terms of precision, reproducibility, and cycle time. This review categorizes and discusses methodologies such as task allocation, reinforcement learning, and Cyber-Physical Systems (CPS)-based planning approaches that facilitate HRC in the assembly process. It also explores experiments and future trends to address challenges and enhance efficiency in manufacturing assembly through intelligent collaboration between humans and robots. The objective of this research is to provide insights and directions for further research in HRC to optimize manufacturing processes. By analyzing the existing state-of-the-art and presenting future prospects, this paper aims to guide researchers and practitioners toward more effective implementations of HRC in manufacturing assembly, ultimately leading to improved operational efficiency and productivity.

In a dynamic and rapidly changing world, customers’ often conflicting demands plus fluid economic requirements, often driven by geo-politics, have continued to evolve, out-striping the capability of existing production systems. With its inherent shortcomings, the traditional factory has proven to be incapable of addressing these modern-day manufacturing challenges. Recent advancements in Industry 4.0 have catalyzed the development of new manufacturing paradigms (or smart factory visions) under different monikers (e.g., Smart factory, Intelligent factory, Digital factory, Cloud-based factory etc.) would help fix these challenges. Due to a lack of consensus on a general nomenclature for these manufacturing paradigms, the term Future Factory (or Factory of the Future) is here used as a collective euphemism, without prejudice. The Future Factory constitutes a creative convergence of multiple technologies, techniques and capabilities that represent a significant change in current production capabilities, models, and practices. It is a data-driven manufacturing approach and system that harnesses intelligence from multiple information streams i.e., assets (including people), processes, and subsystems to help create new forms of production efficiency and flexibility. Serving both as a review monograph and reference companion, this paper details the meanings, characteristics, and technological underpinnings of the Future Factory. It also elucidates on the architectural models that guide the structured deployment of these modern factories with particular emphasis on three advanced communication technologies capable of speeding up advancements in the field. It not only highlights the relevance of communication between assets but also lays out mechanisms to achieve these interactions using the Administration shell. Finally, the paper also discusses the key enabling technologies that are typically embedded into bare bone factories to help improve their visibility, resilience, intelligence, and capacity, in addition to how these technologies are being deployed and to what effect. At the onset of the study, we were interested in developing a monograph which would serve as a comprehensive but concise review of general principles, fundamental concepts, major characteristics, key building blocks and implementation guidelines for the Future Factory within the overall context of the manufacturing ecosystem, in the age of Industry 4.0. Our hope is that this paper would enrich the extant literature on advanced manufacturing, help shape policy and research, and provide insights on how some of the identified pathways can be diffused into industry.

Maker spaces can contribute significantly to the circular economy of cities: they demonstrate not only the economic potential of inner loop circularity, but also provide tangible evidence of spatial and social integration of production into the urban context. This paper presents findings from a typological analysis of 326 maker spaces in seven European cities, with a focus on selected exemplar case studies to reveal design characteristics, principles and opportunities for circular city development. The research shows that circular economy principles of ‘reduce-reuse-recyle’ are aligned with maker spaces such as repair cafés, secondhand shops and fab labs. In the context of cities, circularity is revealed by a spatial tightening of resource cycles that close the loops of product life cycles. Furthermore, urban maker spaces demonstrate social engagement and a relationship to local production which inherently includes maintenance, repair, reuse, and redistribution. This paper defines five maker typologies, presents exemplars of each from different urban contexts and posits hybrid design strategies for the transition to circular maker cities. Through the adoption of these findings into urban planning policy it is possible accelerate circular urban production and close the sustainability gap between small scale local and large scale regional manufacturing.

The Laser Thermal-Joule Heating Composite Process was studied by orthogonal tests based on an analysis of fabrication parameters such as the laser power, wire feeding speed, and electric current. Temperature profiles and the geometric morphology of deposited layers under different process parameters were analyzed, and the overlaps between the layers and the substrate were observed. Results show that when the temperature at the bottom layer of the additive manufacturing is higher than the melting point of the substrate, and the highest temperature at the top layer does not exceed the over-firing temperature, good morphology and close bonding with the substrate can be obtained. Finally, appropriate process parameters were identified and verified to print multiple layers continuously.

The Selective Paste Intrusion (SPI) method is a layer-by-layer additive manufacturing technique that allows for the production of complex geometries in concrete elements by selectively bonding aggregates with cement paste in a particle bed. To create reinforced concrete, the Wire and Arc Additive Manufacturing (WAAM) process shall be integrated into SPI. This technique allows the production of almost free-formed reinforcement and thus complements the advantage of SPI to produce free-formed structures of almost any geometry. However, integration of WAAM into SPI poses a considerable challenge, as high temperatures are generated during the welding process. These temperatures can negatively affect the rheological properties of the cement paste, in turn the penetration behavior of the paste in the particle bed and, subsequently, the mechanical properties of the hardened concrete. A possible passive cooling strategy is to increase the protruding length of the reinforcement bars out of the particle-bed. This requires that the distance of the print nozzle to the particle bed is as well increased, since it must be possible to move it across the reinforcement. The objective was thus to investigate the effect of that distance on print quality and to quantify a maximum allowable distance for an adequate print quality (for the printer setting used) in terms of shape accuracy and concrete strength. Compressive and flexural strength tests as well as geometrical measurements using a 3D scanning method were performed on specimen, printed with varying print nozzle to particle bed distances. It can be stated that for the used SPI print-heads, nozzle-types and parameter settings, the distance between the nozzle and the particle bed should not exceed 50 mm to ensure sufficient print quality in both shape accuracy and mechanical strength.

Functionally graded materials (FGM) have attracted considerable attention in the field of composite materials and rekindled interest in research on composite materials, due to their unique mechanical response achieved through material design and optimization. Compared to conventional composites, FGMs offer several advantages and exceptional properties, including improved deformation resistance, improved toughness, lightness properties, and excellent recoverability. This study focused on the production of functionally graded (FG) polymer materials by additive manufacturing (AM) method. FG structures were produced by the Fused Deposition Modeling (FDM) method using Acrylonitrile benzidine styrene (ABS) and Polylactic acid (PLA) materials and tensile tests were performed according to ASTM D638. The effects of different layer thicknesses, volume ratios, and total thicknesses on mechanical behavior were investigated. In addition, tensile tests were carried out by producing single-layer samples at certain volume ratios to create a numerical model with the Finite element method to verify the experimental data. As a result of this study, it is presented that the FG structure produced with FDM improves mechanical behavior.

Nitrate and nitrite are the most extensively used food additives in the food industry for their multifunctional properties, and they have been in the spotlight for decades for its controversial back effects. The conversion of nitrate and nitrite to various nitroso compounds, specifically N-nitrosamine raises the question of the integrity of these additives. Nitrosamine is a well-known carcinogen and mutagen. A large number of studies demonstrate the carcinogenic effects of these salts. Are these findings credible? Nonetheless, the regulatory agencies approve its usage; is it biased? Are we feeding on the carcinogens in processed foods? The current work provides an insight into the basic and toxicological findings of the food additives -nitrate and nitrite.

In the current paper, we present our work towards developing a solution combining computer vision and AI to support footrest design customization in the wheelchair industry. Wheelchairs and postural systems are complex and often user centric in the design process. Those products require a wide range of adjustments to adapt to the various anthropometries and disabilities of patients. Most commercial products, dedicated to severe disability, also include a series of additional elements, designed to keep the patient in a comfortable but “fixed” position. Such systems do not allow to “fix and maintain” the position of patient foot, especially for patients who have no control over their lower limbs. For that reason, it becomes essential to supply wheelchairs with specific elements to hold patient's shoe in place. Commercial foot supports are typically developed on standard sizes and do not always cover the entire population. In addition, those designs are based on standard shoes, thus they do not lend themselves well to adapting to special shoes or orthopedic shoes. Feet final position therefore becomes a key element in correct postural management. In this paper, we investigate the use of computer vision and AI to correctly define customization parameters of the footrest. The proposed design has been developed based on the specifications received from the Pro Medicare S.r.l orthopedic technicians. We present suitable algorithms, design principles and well defined components towards the implementation of our proof of concept. We also present the testing activities we have undertaken and the obtained performance results.

Sulphites are the most widely used food additives for their versatile applications in processing; perhaps we are negligence of its shortcomings. On account of various research studies, sulphites induce serious allergic reactions and other health alignments, but still regulatory agents permit its usage. Is it a wiser choice? The purpose of this paper is to clear up ambiguity and provide a clear view on why sulphites are employed in the food industry, why its usage is restricted, and why sulphite is a toxicant

In this study composite with alternate layers of 5056 and 1580 alloys was manufactured by wire arc additive manufacturing technology. It is shown that increased strength characteristics of composite material can be obtained due to deformation treatment by high-pressure torsion (HPT) technique. The microstructure and mechanical properties of the HPT-processed material in different structural states were investigated. The HPT-processed material exhibits high value of ultimate tensile strength (~ 770 MPa) but low ductility. Short-term annealing at 250 oС and additional deformation by HPT to 0.25 of revolution at room temperature resulted in a slight decrease in material’s strength to ~ 700 MPa but provided ductility ∼ 9%. Physical mechanisms to improve plasticity in correlation with microstructure evolution is discussed.

The present paper deals with research of maintenance strategy and maintenance process improvement procedure to increase the efficiency and quality of the production system. It includes the design of maintenance performance indicators relevant to today's manufacturing systems to track improvements in the sustainable development of the manufacturing system. Describes the basic principles of maintenance strategy with linkages to the manufacturing system and selecting the right strategy for the organization. The paper emphasizes maintenance management auditing and quality implementation in maintenance. Further, there is the process of changing the maintenance strategy described. This process includes reassessing the criticality of machines and equipment and their design units, then resource and capacity planning and input for maintenance management. It is described the impact of changing the maintenance management organization and maintenance capacity in the light of the change of strategy and the evaluation of maintenance choice alternatives, together with an analysis of the optimization procedure. The paper concludes by presenting the achieved impact of maintenance strategy change on the production efficiency and production quality of the selected enterprise.

Three-dimensional (3D) printing technology, also known as additive manufacturing (AM), has emerged as an attractive state-of-the-art tool for precisely fabricating functional materials with complex geometries, championing several advancements in tissue engineering, regenerative medicine, and therapeutics. However, this technology has an untapped potential for biotechnological applications, such as sensor and biosensor development. By exploring these avenues, the scope of 3D printing technology can be expanded and pave the way for groundbreaking innovations in the biotechnology field. Indeed, new printing materials and printers would offer new possibilities for seamlessly incorporating biological functionalities within the growing 3D scaffolds. Herein, we review the extrusion-based 3D printing modalities emphasizing biosensor applications. We highlighted the application of natural, synthetic, and composite biomaterials as 3D-printed soft hydrogels. Emphasis was placed on the approach by which the sensing molecules were introduced during the fabrication process. Finally, the future perspective was provided.

Artificial Intelligence models are increasingly used in manufacturing to inform decision-making. Responsible decision-making requires accurate forecasts and an understanding of the models’ behavior. Furthermore, the insights into models’ rationale can be enriched with domain knowledge. This research builds explanations considering feature rankings for a particular forecast, enriching them with media news entries, datasets’ metadata, and entries from the Google Knowledge Graph. We compare two approaches (embeddings-based and semantic-based) on a real-world use case regarding demand forecasting.

This paper presents a systematic approach to developing big data analytics for manufacturing process-relevant decision-making activities from the perspective of smart manufacturing. The proposed analytics consists of five integrated system components: 1) data preparation system, 2) data exploration system, 3) data visualization system, 4) data analysis system, and 5) knowledge extraction system. The functional requirements of the integrated systems are elucidated. In addition, JAVA- and spreadsheet-based systems are developed to realize the proposed integrated system components. Finally, the efficacy of the analytics is demonstrated using a case study where the goal is to determine the optimal material removal conditions of a dry electrical discharge machining operation. The analytics identified the variables (among voltage, current, pulse-off time, gas pressure, and rotational speed) that effectively maximize the material removal rate. It also identified the variables that do not contribute to the optimization process. The analytics also quantified the underlying uncertainty. In synopsis, the proposed approach results in transparent, big-data-inequality-free, and less resource-dependent data analytics, which is desirable for small and medium enterprises—the actual sites where machining is carried out.

From the perspective of manufacturing companies, the political, media and economic discourse on decarbonisation of the recent years manifests itself as an increasing social expectation of action. In Germany in particular, this discourse is also being driven forward by powerful companies, respectively sectors, most notably the automotive industry. Against this background, it was examined how German manufacturing companies react to rising societal pressure and emerging policies. It is examined which measures the companies have taken or plan to take to reduce their footprint, which aspirations are associated with this and by which structural characteristics (company size, energy intensity, sector) these are influenced. A mix methods approach was applied, utilising data gathered from approx. 900 companies in context of the Energy Efficiency Index of German Industry (EEI), along with media research focusing on decarbonisation plans and initiatives announced. We demonstrate that one-size-serves-all approaches are not suitable to decarbonise industry as the situation and ambitions differ considerably depending on size, energy intensity and sector. Even though the level of ambition and urgency is high, particularly micro and energy intensive companies are challenged. The research uncovers a series of questions that call for attention to materialise the ambitions and address the challenges outlined.

With the development of artificial intelligence technology, manipulators, as an important part of the work of intelligent robots, can replace humans to perform various complex tasks. In recent years, there have been many studies on the remote control of manipulators using various control technologies. Starting from the manufacturing materials of flexible sensors used for manipulators, this paper introduces the base materials, sensing materials, and flexible electrode materials of flexible sensors, respectively, and summarizes the performance of flexible sensors made based on the characteristics of different materials. The basic principles of dipping and coating, lithography, inkjet printing, screen printing, 3D printing and so on are introduced from the perspective of flexible sensor manufacturing process. Some different functions of sensors achieved by different structural designs, such as: tensile cracking, microchannels, etc. are also introduced. Then, from the perspective of recognition accuracy of the manipulator, two kinds of control methods based on flexible sensor by data glove control and surface EMG control are classified and introduced, and the problems existing in three aspects of flexible sensor material preparation, manufacturing process and control series design are summarized. Finally, the possible future research directions in this field are suggested.

Metal powder bed fusion (PBF) additive manufacturing (AM) builds metal parts layer by layer upon a substrate material. The strength of this interface between substrate and printed material is important to characterize, especially in applications where the substrate is retained and included in the finished part. This paper studied the tensile and torsional strengths of wrought and additively manufactured (through PBF) SS316L and compared them to specimens composed of half wrought material and half PBF material. The PBF specimens consistently exhibited higher strength and lower ductility than the wrought specimens. The hybrid PBF/wrought specimens performed similarly to the wrought material. In no specimens did any failure appear to occur at or near the interface between wrought substrate and PBF material. In addition, most of the deformation in the PBF/wrought specimens appeared to be limited to the wrought portion of the specimens. These results are consistent with microscopy showing smaller grain size in the PBF material, which often leads to increased strength in SS316L due to the Hall-Petch relationship.

How to effectively suppress thermal cracks in the metal laser additive manufacturing process is still one of the key issues to be solved in the field of laser additive manufacturing. Metal tin, with wide liquid phase working temperature range, high boiling point, low viscosity, high thermal conductivity and excellent electrical conductivity. The use of tin as an auxiliary thermal management material in the metal additive manufacturing process is expected to achieve effective regulation of the temperature field and stress field of the formed part, thereby inhibiting the initiation of cracks and obtaining formed parts with the target grain structure and high reliability. This paper presented a novel liquid metal-assisted laser additive manufacturing method (LMAAM). A numerical model for the laser additive manufacturing of tin-assisted titanium alloys was established. The differences of the flow field, temperature field and stress field of the formed parts with tin and without tin were compared and analyzed. The influence of the interaction position between the tin liquid level and the forming part on the temperature field and stress field of the forming part was deeply studied. The laser additive manufacturing experiment of tin-assisted titanium alloy was carried out, and the experimental results were basically consistent with the simulation results, which verified the validity of the model. LMAAM technology has proven to be an effective method for additive manufacturing of highly reliable formed parts.

Background: Residents of a large area of North-Eastern Italy were exposed for decades to high concentrations of perfluoroalkyl and polyfluoroalkyl substances (PFAS) via drinking water. Despite the large amount of evidence in adults of a positive association between serum PFAS and metabolic outcomes, studies focusing on children and adolescents are limited. We evaluated the associations between serum PFAS concentrations and lipid profile, blood pressure and body mass index (BMI) in highly exposed adolescents and children. Methods: A cross-sectional analysis was conducted in 6669 adolescents (14-19 years) and 2693 children (8-11 years) enrolled in the health surveillance program of the Veneto Region. Non-fasting blood samples were obtained and analyzed for perfluorooctanoic acid (PFOA) perfluorooctane sulfonate (PFOS), perfluorohexanesulfonic acid (PFHxS), perfluorononanoic acid (PFNA), total cholesterol (TC) high-density lipoprotein cholesterol (HDL-C) and triglycerides. Low-density lipoprotein cholesterol (LDL-C) was calculated. Systolic and diastolic blood pressure (BP) were measured and BMI z-score accounting for age and sex was estimated. The associations between ln-transformed PFAS (and categorized into quartiles) and continuous outcomes were assessed using generalized additive models. Analyses were stratified by gender and adjusted for potential confounders. Results: Among adolescents, significant associations were detected between all investigated PFAS and TC, LDL-C, and to a fewer extent HDL-C. Among children, PFOS and PFNA had significant associations with TC, LDL-C and HDL-C, while PFOA and PFHxS had significant associations with HDL-C only. Increased serum concentrations of PFAS, particularly PFOS, were associated with decreased BMI z-score. No statistically significant associations were observed between PFAS concentrations and BP. Conclusions: Our study supports a consistent association between PFAS concentration and serum lipids, stronger for PFOS and PFNA and with a greater magnitude among children compared to adolescents, and a negative association of PFAS with BMI.

The paper presents a thorough examination of the burgeoning field of utilizing manufacturing waste for sustainable energy generation, aligning with the global imperative for resource efficiency and clean energy solutions. With the manufacturing sector being a significant contributor to waste generation, this study explores innovative approaches to transform waste materials into valuable resources, thereby mitigating environmental impacts and contributing to a circular economy. This work critically reviews current trends, emphasizing technological advancements, policy interventions, and market dynamics shaping the manufacturing waste utilization landscape. Noteworthy developments, such as advanced sorting technologies and the integration of digital solutions, are discussed in detail, showcasing their role in enhancing the efficiency of waste management processes. The study outlines future trends in the field, anticipating a shift towards closed-loop systems guided by circular economy principles. This comprehensive review aims to contribute to the discourse on sustainable waste management and energy generation by providing a holistic perspective on the field's current state and offering insights into the future trajectories that will propel manufacturing waste toward a more sustainable and circular future. The synthesis of technological innovation, collaborative efforts, and evolving regulatory frameworks presents a compelling case for the continued exploration of manufacturing waste as a valuable resource in the pursuit of a more sustainable and energy-efficient world.

In this paper, we present an experimental procedure to enhance the dimensional accuracy of fabrication via Stereolithography (SLA) of features at the micro-scale. Using a custom benchmark part, deviations in feature dimensions (micro-pores diameters and depths) were detected and measured by confocal microscopy. Samples characterization and experimental observations allowed the identification of inaccuracy sources, mainly due to the laser beam scanning strategy and to the incomplete removal of uncured liquid resin in post-processing (i.e., IPA washing). As a technology baseline, the measured dimensional errors on pores diameters were up to -46%. A compensation method of the nominal laser spot diameter (85m) was defined and implemented resulting in relevant improvements in dimensional accuracy of 9.8% and 11.3% on full-open pores diameter and depth, respectively. Further investigation was performed on a customized test part to adjust the calibration procedure. The measurements revealed that the estimate of the laser beam spot size is 968m. Measurements on micro-pores having different sizes revealed that a constant compensation parameter (i.e., C=85, 96, 120m) is not fully effective at the micro-scale, where average errors still remain at -24%, -18.8%, and -16% for compensations equals to 85, 96 and 120m, respectively. A further experimental campaign allowed the identification of an effective nonlinear compensation law where the compensation parameter depends on the micro-feature size C= f (D). Results show a sharp improvement in dimensional accuracy on micro-pore fabrication, with errors consistently below +8.2%. The proposed compensation method can be extended for the fabrication of any micro-features without restrictions on the specific technology implementation. Concerning the inaccuracy introduced by the laser path, experimentation with a customized part allowed the optimization of the part orientation to reduce the errors and their differences along the X and Y machine axes.

The most modern technique utilized to create intricate and complexly manufactured parts for a variety of applications is called additive manufacturing (AM). Fused deposition modeling (FDM) has received the greatest attention in the development and manufacturing sectors. The main objective of this study was to investigate how printing factors affected the mechanical characteristics of printed samples. Samples were produced using an FDM 3D printer in compliance with ASTM D638 using a variety of input settings, including orientation, layer thickness, speed, and infill pattern. On the printed samples, tensile tests and morphological analysis using a scanning electron microscope (SEM) were done. The findings of this study demonstrate that factors including layer thickness, printing speed, and orientation significantly affect the tensile strength of ABS printed samples. Discovering the process settings that could lead to superior mechanical and physical characteristics would undoubtedly assist designers and manufacturers worldwide as the FDM 3D printer becomes more and more crucial in manufacturing engineering parts.

Porous structured metallic implants are preferable as bone graft substitutes due to their faster tissue integration mediated by bone in-growth and vascularization. The porous scaffolds/ implants should also mimic the graded structure of natural bone to ensure a match of mechanical properties. This article presents a method to design graded porous structured acetabular implant and identifies the suitable parameters for manufacturing the model through additive manufacturing. The design method is based on slice-wise modification to ensure continuity of gradation. Modification of the slices was achieved through the binary image processing route. A geodesic dome type design was adopted for developing the acetabular cup model from the graded porous structure. The model had a solid shell with the target porosity and pore size gradually changing from 65% and 950 µm, respectively, in the inner side to 75% and 650 µm, respectively, towards the periphery. The required dimensions of the unit structures, and the combinations of pore structure and strut diameter to obtain the target porosity and pore size were determined analytically. Suitable process parameters were identified to manufacture the model by Direct Metal Laser Sintering (DMLS) using Ti6Al4V powder after carrying out a detailed experimental study to minimize the variation of surface roughness and warping over different build angles of the strut structures. A dual contour scanning was implemented to simplify the scan strategy. The minimum diameter of struts that could be manufactured using the selected scanning strategy and scanning parameters was found to be 375 µm. Finally, the model was built and from the micro-CT data, the porosities and pore sizes were found to be closely conforming to the designed values. The stiffness of the structures, as found from compression testing, was also found to be well matching with that of human trabecular bone. Further, the structure exhibited compliant bending-dominated behavior under compressive loading.

The operation status of manufacturing equipment is directly related to the reliability of the operation of manufacturing equipment and the continuity of operation of the production system. Based on the analysis of the operation status of manufacturing equipment and its characteristics, it is proposed that the concept of assessing the operation status of manufacturing equipment can be realized by applying real-time acquisition of accurate inspection data of important parts of weak motion units and comparing them with their motion status evaluation criteria. Through the study of the application characteristics of fractional-order calculus theory, a differential data fusion model based on the fractional-order differential operator is established. The advantages of Internet of Things (IoT）technology and fractional order differential fusion algorithm are integrated to obtain real-time high-precision data of the operating parameters of manufacturing equipment, and the research objective of operating condition assessment of manufacturing equipment is realized. The feasibility and effectiveness of the method are verified by the application of the method in machining center operation status assessment.

The performance of a small reconfigurable unmanned aerial vehicle (UAV) is evaluated, 1 combining a multidisciplinary approach in computational analysis of additive manufactured struc- 2 tures, fluid dynamics, and experiments. Reconfigurable UAVs promise cost savings and efficiency 3 without sacrificing performance, while demonstrating versatility to fulfill different military mission 4 profiles. The use of computational fluid dynamics (CFD) in UAV design produces higher accuracy 5 aerodynamic data, which is particularly important for complex aircraft concepts such as blended 6 wing bodies. To address challenges relating to anisotropic materials, the Tsai-Wu failure criterion is 7 applied to structural analysis, using CFD solutions as load inputs. Aerodynamic performance results 8 show the low-speed variant attains an endurance of 1 hour, 48 minutes, whereas for its high-speed 9 counterpart, it is 29 minutes at 66.7% higher cruise speed. Each variant serves different aspects of 10 small UAS deployment in combat, with low-speed envisioned for close surveillance, and high-speed 11 for incursions. Experimental and simulation results suggest room for design iteration, in wing area 12 and geometry adjustments. Structural simulations demonstrated the need for airframe improvements 13 in the low-speed configuration. This paper highlights the potential of reconfigurable UAVs to disrupt 14 the industry, advocating for further research and design improvements.

This study investigates the critical success factors (CSFs) of Lean Six Sigma (LSS) implementation and their impact on organizational growth in Nigerian enterprises. The objectives were to identify crucial CSFs for successful LSS deployment and assess their significance in achieving organizational growth. A mixed-method approach was used, including literature analysis, expert judgments, and a survey of 130 participants from various industries. The survey questionnaire employed a 5-point Likert scale to collect data on LSS CSFs and implementation success metrics. Structural equation modeling (SEM) with SmartPLS 4.0 analyzed the data, revealing that LSS CSFs significantly contribute (up to 39.2 percent) to the success of LSS implementations. The study identified 12 critical LSS CSFs, categorized into workers, business, and management, each influencing LSS implementation. Recommendations include creating a supportive environment, providing comprehensive training, and prioritizing continuous improvement efforts for successful LSS deployment. Future research possibilities include longitudinal studies, sector-specific analyses, and qualitative investigations to deepen understanding and enhance LSS implementation outcomes. In conclusion, successful LSS implementation empowers Nigerian organizations with improved productivity, waste reduction, and sustainable production systems, leading to overall growth and success.

Surfaces of additively manufactured metal parts from powder-based processes typically show powder particle agglomerations and other features, resulting in high surface roughness. Proper characterization of those surfaces is necessary in order to assess part quality with respect to coatability, mechanical performance or corrosion resistance for use in aerospace, automotive, medical and more industrial applications. Optical surface texture measurement allows for collection of areal surface data, while the established contact stylus method only captures line profile data. When applying optical methods for surface topography measurements, proper data acquisition and post-processing in order to assess surface texture may be complex. A number of variables can be adjusted, such as different measurement settings, approaches to outlier removal, evaluated area size or form removal. This work shows the influence of selected z-range prior to measurement and the influence of choosing pre-defined outlier removal settings in MountainsMap 9.2 on selected ISO 25178-2:2022 parameters calculated from data obtained from confocal microscopy for as-built Ti6Al4V from laser powder bed fusion. The aim is to show the influence of variation in measurement and post-processing on calculated surface texture parameters and stress the importance of proper documentation in order to achieve reproducibility of data for quality management.

The osseointegration process in and around additively manufactured (AM) lattice structures of a new titanium alloy, Ti–19Nb–14Zr, was evaluated. Three different implants, including lattices with increasing high sidewalls gradually closing them, were designed, manufactured and implanted in the tibia and metatarsal bone of two sheep for twelve weeks. After removal, they were characterized with X-ray computed tomography (XCT). The 3D XCT images were segmented using machine learning. The bone-interface implant (BII) and bone-implant contact (BIC) were studied. The results show that, since AM naturally leads to high roughness surface finish, the wettability of the implant is increased. The new alloy possesses an increased affinity to the bone enhancing the quality of osseointegration. The lattice provides crevices, in which the biological tissue can jump in and cling. The combination of these factors is pushing ossification beyond its natural limits. Therefore, the quality and speed of the ossification and osseointegration in and around these Ti–19Nb–14Zr AM laterally closed lattice implants open the possibility of bone spline key of prostheses. This enables the stabilization of the implant into the bone while keeping the possibility of punctual hooks allowing the implant to be removed more easily if required.

This study is focused on the mechanical properties of WC-Co composites obtained via Selective Laser Sintering using PA12 as a binder. The as-printed samples were thermally debonded, and sintered, first in vacuum, and then sinter-HIPed at 1400oC, using 50 bar Ar, which has led to relative densities up to 66 %. Optical metallographic images show a microstructure consisting of WC, with an average grain size in the range of 1.4 – 2.0 µm, with isolated large grains, in a well-distributed Co matrix. The shrinkage of the samples was 43 %, with no significant shape distortion. The printing direction of the samples has a great impact on the transversal rupture strength (TRS). Nevertheless, the bending strength was low, with a measured maximum of 612 MPa. SEM images of the fracture surface of TRS samples show the presence of defects that constitute the cause of the low measured values. The hardness values position the obtained composites in the range of medium coarse classical cemented carbides. The results were also related to the amount of free Co after sintering, close to the initial one, as assessed by magnetic measurements, indicating a low degree of interaction with PA12 decomposition products.

The use of additive manufacturing in fabricating composite components has been gaining traction in the past decade. However, some issues with mechanical perfor-mance still need to be resolved. The issue of material porosity remains a pertinent one that needs more understanding to be able to determine viable solutions. Different re-searchers have examined the subject of porosity issues in AM-fabricated CFRP compo-sites. However, more research to quantitatively determine the effects of fabrication temperatures at the micro-scale is still needed. This study employed micro CT scan analysis to quantitatively compare the effects of fabrication temperatures at 230°C, 250°C, 270°C, and 290°C for CF-PA and CF-ABS composites. This followed an SEM evaluation which was used to determine the effects of the temperatures on interlayer and intralayer porosity generation. The porosity volume was related to the mechanical properties results in which it was determined how deposition temperatures affect the porosity volumes. It was also determined that semicrystalline composites are generally more affected by fabrication temperatures than amorphous composites, with the rela-tionship between porosity and mechanical properties also established. The overall po-rosity volume from the interlayer and intralayer voids was also determined, with the interlayer voids found to play a more determinant role in influencing the mechanical properties.

Sri Lanka is a top producer of premium quality concentrated latex (CL), which becomes a base material for dipped rubber products such as gloves and condoms. The processing of CL is resource-intensive, requiring significant amounts of energy, fuel, water, and chemicals. This process leads to various environmental issues such as wastewater pollution, malodor, and greenhouse gas emissions. Several environmental life cycle assessments (ELCA) have been conducted at international and local levels to address the aforesaid issues. However, ELCAs encapsulating different environmental impact areas on CL processing in Sri Lanka are absent. The study revealed that electricity usage was the main hotspot of the environmental burden, significantly impacting Abiotic depletion (fossil fuels), Global warming potential, Ozone layer depletion, Photochemical oxidation, and Acidification. Heavy reliance on coal in the Sri Lankan power grid was identified as the root of this trend. The study suggested two viable options to mitigate the environmental impact: installing inverters to centrifuge separators and solar systems in the factories. The second option was deemed more effective, reducing Acidification, Photochemical oxidation, and Global Warming Potential by approximately 37%, 36%, and 28%, respectively. Relevant officials may immediately consider these improvement options and collaborate to pave the way to a sustainable natural rubber industry.

In recent years, additive manufacturing of products made from 5000 series alloys has grown in popularity for marine and automotive applications. In this work, we compare of mechanical properties characteristics of aluminum alloy 5056 material produced by wire-arc additive technology and rolling. We carried out tension tests under quasi-static loading and impact toughness tests under impact loading. The results show that upon similar ultimate tensile strength and ductility, material after additive manufacturing demonstrates decreased resistance against dynamic loading than material manufactured by the typical method of casting and rolling. This decrease in properties was explained through macro- and microstructure analysis.

There is a fast-growing interest in the use of selective laser melting (SLM) for metal/alloy additive manufacturing. Our current knowledge of SLM printed 316 stainless steel (SS316) is limited and sometimes appears sporadic, presumably due to the complex interdependent effects of a large number of process variables of the SLM processing. This is reflected from the discrepant findings in the crystallographic textures and microstructures in this investigation with those reported in the literatures, which also vary itself. The as-printed material is macroscopically asymmetric in both the structures and crystallographic textures. The <101> and <111> crystallographic directions align parallel with the SLM scanning direction (SD) and build direction (BD), respectively. Like-wise, some characteristic low angle boundary features are reported crystallographic, while this investigation unequivocally proves them non-crystallographic since they always maintain an identical alignment with the SLM laser scanning direction irrespective of the matrix material’s crystal orientation. There is also 500±200 nm columnar or cellular features, depending on the cross-section, generally found all over the sample. These columnar or cellular features are formed with walls made of dense packing of dislocations entangled with Mn, Si and O enriched amorphous inclusions. They remain stable after the ASM solution treatments at 1050 °C temperatures, and therefore, are capable of hindering boundary migration events of recrystallization and grain growth. Thus, the nanoscale structures can be retained at high temperatures. Large 2-4 µm inclusions form during the solution treatment, within which the chemical and phase distribution are heterogeneous.

Investment management is a common process and practice used for achieving a desirable investment goal or outcome. Investment assessment should be carried out at various stages of project realization in accordance with capital investment volume. Investment risk management implies the effective control of all procedures and monitoring of risks in all phases of the investment project. Because of the reason that a single indicator in probability calculation of achieving optimal return from the investment does not exist, performing sufficiently reliable estimates of the quality of investments becomes a tedious task. There are many indicators, factors, and criteria required for consideration to reach the effective solution of the investment problem. Unfortunately, the systematic variation of economic situations in the marketplace stipulates the continuous and frequent changes of investment conditions and environment in which the investor should act and operate. Hence, the rules required for providing a reasonable quality of investment projects can be based only on investor’s management strategy and rely on investor’s intuition and practice. The importance of classification in investment management and decision making process is undeniable. The objects to be classified are described using assessments in accordance with various criteria which can be both quantitative and qualitative. With a competent formulation of the investment process, both methods are used in parallel. There exist various decision making approaches for the investment management, and simple additive weighting (SAW) is one of the well-known multi-criteria decision making (MCDM) methods aiming to provide an optimal decision for decision maker when solving various real-life problems, and particularly, investment problem. In this paper, fuzzy simple additive weighting (FSAW) method in group decision making is applied to undertake the capital investment expenditure for purchasing cars with the purpose of renting them to the public. The development of existing FSAW method is accomplished and this process involves the sensibility of outcomes to changes in the rate of fuzziness represented with decisions taken. Eventually, the degree of fuzziness involved in an analysis that directly attempts to model the immanent vagueness and imperfectness in particular precedency judgments made, is determined. A numerical example illustrates the importance and effectiveness of the suggested approach with the aim of ranking alternatives and hence, determining the most preferred alternative in MCDM problem.

As China put forward its “carbon emissions peak and carbon neutrality” goals, how to achieve carbon reduction had become a key for China’s goal. The manufacturing industry is an important source of carbon dioxide emissions. For a manufacturing country like China, adjustments in various aspects of the industry would have a huge impact on carbon emissions. As an important reform of contemporary production mode, the process of production automation in China will inevitably affect China's carbon emissions. Therefore, the analysis of the impact of production automation on carbon dioxide emissions was an important basis for judging the future carbon reduction in China. Refer to the traditional study of carbon Kuznets curve, this paper analyzed the impact of average wage on production automation and the role of production automation in the carbon Kuznets curve(CKC). This paper proposed that production automation plays a mediating role in the process of carbon emissions, and gives a verification model of the mediating role. By analyzing the relationship between average wage and production automation process, the U-shaped curve relationship between them was verified. By examining the relationship between carbon dioxide emission data and production automation industry in China, we verified that production automation plays a partial mediating role in the change of carbon Kuznets curve. Combined with the analysis of the two parts, this paper believed that with the continuous development of China's intelligent manufacturing industry, China's carbon reduction prospects were more optimistic, and there was a good industrial foundation to achieve the “carbon peaking and carbon neutrality” goals. Finally, this paper proposes policy suggestions as increase research investment in production automation, help promote the application of production automation, encourage the research and application development of low-carbon technology, especially encourage modular design, so as to give full play to the role of production automation in the process of carbon neutrality in China.

This paper presents an original method of additive manufacturing of cylindrical parts with variable circumference thickness, which allows the control of the deposition of molten material using an algorithm for decomposing the part geometry into volumetric elements with known dimensional configuration. In the absence of a post-processor capable of controlling additive manufacturing on a 5-axis numerical control machine, control of the deposition of molten material is done using parameterized programs, which can control both the feed speeds of the machine tool axes and the specific functions of the printing equipment. Additive manufacturing can make a positive contribution to sustainable development compared to traditional manufacturing technologies, thus making a positive contribution for a sustainable future. The aim of the work is to make it possible to 3D print parts with variable wall thickness using a CNC machining centre. To obtain the variable thickness layer we have implemented an original method of deposition with molten material (FDM), the coordination of the system composed of physical elements respectively programmable elements, is realized through control functions materialized in parameterized part programs, the generated outputs being the variable speed of the machine axes on a circular trajectory, the angular positioning, the filament advance.

In this study, 3D printing of poly-l-lactic acid (PLLA) scaffolds reinforced with graphene oxide (GO) nanoparticles via Digital Light Processing (DLP) was investigated to mimic bone tissue. Stereolithography is one of the most accurate additive manufacturing method, but the dominant available materials used in this method are toxic. In this research, a biocompatible resin (PLLA) was synthetized and functionalized to serve the purpose. Due to the low mechanical properties of the printed product with the neat resin, graphene oxide nanoparticles in three levels (0.5, 1, and 1.5 Wt.%) were added with the aim of enhancing the mechanical properties. At first, the optimum post cure time of the neat resin was investigated. Consequently, all the parts were post-cured for three hours after printing. Due to the temperature-dependent structure of GO, all samples were placed in an oven at 85 ° C for different time periods of 0, 6, 12, and 18 hours to increase mechanical properties. The compression test of heat treated samples reveals that the compressive strength of the printed parts containing 0.5,1, and 1.5 % of GO increased by 151,162 ad 235%, respectively. Scaffolds with the designed pore sizes of 750 microns and a porosity of 40% were printed. Surface hydrophilicity test was performed for all samples showing that the hydrophilicity of the samples increased with increasing GO percentage. The degradation behavior of the samples was evaluated in a PBS environment, and it revealed that by increasing GO, the rate of component degradation increased, but the heat treatment had the opposite effect and decreased the degradation rate. Finally, besides improving biological properties, a significant increase in mechanical properties under compression can introduce the printed scaffolds as a suitable option for bone implants.

This paper presents a systematic literature review that identifies critical factors impacting the implementation of advanced manufacturing technology (AMT) worldwide. The study utilizes two databases, ProQuest and Compendex, as well as Google Scholar. The study identified eight dimensions that illustrate the critical factors of AMT adoption and implementation: education, planning, top management know-how, technical know-how, business, economic impact, regulations, and social impact. The results highlight a research gap in understanding the need for effective integration among these eight dimensions in developed and developing economies. Consequently, the study recommends the adoption of a broader perspective that considers the role of integration and interaction between critical factors in each category and their impact on AMT implementation. The systematic literature review conducted in this study reviews several critical factors related to the adoption and implementation of AMT.

The recent decade has witnessed the evolution of a novel paradigm of alloying which is based on utilising multiple elements to design compositionally complex alloys also known as high entropy alloys (HEAs). Conventional manufacturing of HEAs has a number of drawbacks, especially in terms of mechanical properties and design complexities. This has been addressed by additive manufacturing (AM), which has not only led to the fabrication of complex-shaped HEA components but has also enabled both ex-situ and in-situ tailoring of alloy microstructures. Considering the increasing interest in AM-based fabrication of HEAs in the last ten years, the present chapter is aimed at highlighting the present status and challenges in the avenue of AM of HEAs. This has been followed by a discussion on the recent trends in the avenue of AM-based fabrication of HEAs from the viewpoints of (i) microstructure evolution and mechanical properties, and (ii) alloy fabrication techniques. The chapter ends with a discussion on the future prospects in the aforementioned avenue.

The future of intelligent manufacturing machines involves autonomous selection of process parameters to maximize productivity while maintaining quality within specified constraints. To effectively optimize process parameters, these machines need to adapt to existing uncertainties in the physical system. This paper proposes a novel framework and methodology for feedrate optimization that is based on a physics-informed data-driven digital twin with quantified uncertainty. The servo dynamics are modeled using a digital twin, which incorporates the known uncertainty in the physics-based models and predicts the distribution of contour error using a data-driven model that learns the unknown uncertainty on-the-fly by sensor measurements. Using the quantified uncertainty, the proposed feedrate optimization maximizes productivity while maintaining quality under desired servo error constraints and stringency (i.e., the tolerance for constraint violation under uncertainty) using a model predictive control framework. Experimental results obtained using a 3-axis desktop CNC machine tool and a desktop 3D printer demonstrate significant cycle time reductions of up to 38% and 17% respectively, while staying close to the error tolerances compared to the existing methods.

Additive Manufacturing and nanotechnology have been used as basic tools for the manufacture of nanostructured parts with magnetic properties to expand the variety of applications in additive processes by tank photopolymerization. Magnetic cobalt ferrite (CoFe2O4) and barium ferrite (BaFe12O19) nanoparticles with the size distribution of average value DTEM of 12 ± 2.95 nm and 37 ± 12.78 nm, respectively were generat-ed by hydroxide precipitation method. The dispersion of the nanoparticles on commercial resins (Anycubic Green and IRIX White resin) was obtained by mechanochemical reactions carried out in an agate mortar for 20 minutes, at room temperature and with limited exposure to light. The product of each reaction was placed in amber vials, also being kept in a box, to avoid contact with light. The photopolymerization process was carried out only at low concentrations (w/w % nanoparticles/resin) since, at high concentrations, there is no for-mation of pieces due to the high refractive index of ferrites. Raman shift spectroscopy of the final pieces showed that they contain the magnetic nanoparticles, with no apparent chemical changes. The EPR results of the pieces maintain the magnetic properties and apparently, they are not modified during the photopolymer-ization. Although significant differences were found in the dispersion process of the nanoparticles in each piece, we determined that the photopolymerization did not influence the structure and superparamagnetic behavior of ferrite nanoparticles during processing, and the magnetic properties were successfully transferred to the final 3D-printed magnetic obtained piece.

Quality control is a critical component in industrial manufacturing, directly influencing efficiency, product reliability, and ultimately, customer satisfaction. In the dynamic environment of industrial manufacturing, traditional methods of inspection may not adequately meet the evolving complexity, necessitating innovative approaches to bolster precision and productivity. In this study, we explore the application of mixed reality (MR) technology for real-time quality control in the assembly process. Our methodology involved the integration of smart glasses with a server-based image recognition system, designed to conduct real-time component analysis. The innovative aspect of our study lies in the harmonization of MR and computer vision algorithms, providing immediate visual feedback to inspectors and thereby improving the speed and accuracy of defect detection. YOLOv8 have been adopted in this study for detection object model. The project implementation occurred in a controlled environment to enable a comprehensive evaluation of the system functionality, the identification of possible problems and improvements in the system performance. The results indicated the viability of mixed reality as a powerful tool for enhancing traditional inspection processes. The fusion of MR and computer vision offers possibilities for future advancements in industrial quality control, paving the way for more efficient and reliable manufacturing ecosystems.

Additive manufacturing (AM) technologies show potential for the development of functionally integrated lightweight designs, biomimetic structures and material savings. Typically, as-built surfaces show powder particle agglomerations and re-entrant features, leading to rough surfaces, which are associated with poor fatigue performance. To benefit from the full range of advantages with special focus on aerospace applications, critical features for crack initiation when subjected to fatigue loading need to be identified and mitigated. A first step toward achieving this goal is the surface texture characterisation based on the quantification of surface features. In this paper, selected areal height, functional and feature parameters from ISO 25178-2:2022 are generated and process-specific features are examined for as-built AlSi7Mg0.6 from laser powder bed fusion (PBF-LB). A connection with the particle size distribution of the used powder is demonstrated. It is shown that surface feature analysis opens up opportunities to use physically meaningful surface characteristics in future quality assurance and part qualification processes.

There is evidence that Additive Manufacturing plays a crucial role in the fourth industrial revolution. The design freedom provided by this technology is disrupting limits and rules from the past enabling engineers to have new products otherwise unfeasible. Recent developments in the field of SLM have led to a renewed interest in lattice structures that can be produced non-stochastically in previously unfeasible dimensional scales. One of the primary applications is aerospace engineering where the need of lightweight and performance is urgent to reduce the carbon footprint of civil transport around the globe. Of particular concern is fatigue strength. Being able to predict fatigue life in both LCF (Low Cycle Fatigue) and HCF (High Cycle Fatigue) is crucial for a safe and reliable design in aerospace systems and structures. In the present work, an experimental evaluation of compressive-compressive fatigue behavior has been performed to evaluate the fatigue curves of different cells, varying sizes and relative density. A Design of Experiment approach has been adopted in order to maximize the information extractable in a reliable form.

Nature provides plenty of good examples for simple and very efficient joint assemblies. One example is the enormously flexible cervical spine of American barn owls, which consists of 14 cervical vertebrae. Each pair of vertebrae produces a comparatively small individual movement in order to provide a large overall movement of the entire cervical spine. The biomimetic replication of such joints is difficult due to the delicate and geometric unrestricted joint shapes as well as the muscles that have to be mimicked. Using X-ray as well as micro computed tomography images and with the utilisation of additive manufacturing, it is possible to produce the owl neck vertebrae in scaled-up form, to analyse them and then to transfer them into technically usable joint assemblies. The muscle substitution of these joints is realised by smart materials actuators in the form of shape memory alloy wire actuators. This actuator technology is outstanding for its muscle-like movement and for its enormous energy density [1,2]. The disadvantage of this wire actuator technology is the low rate of contraction, which means that a large length of wire has to be installed to generate adequate movement. For this reason, the actuator wires are integrated into additively manufactured carrier components to mimic the biological joints. This results in joint designs that compensate for the disadvantages of the small contraction of the actuators by intelligently installing large wire lengths on comparatively small installation spaces, while also providing a sufficient force output. With the help of a test rig, the developed technical joint variants are examined and evaluated. This demonstrates the technical applicability of this bionic joints.

The delivery of therapeutical molecules through the skin, particularly to its deeper layers is impaired due to the stratum corneum layer, which acts as barrier to foreign substances. Thus, for the past years, scientists have focused on the development of more efficient methods to deliver molecules to skin distinct layers. Microneedles, as a new class of biomedical devices, consists on an array of microscale needles. This particular biomedical device had been drawing attention due to their ability to breach the stratum corneum, forming micro-conduits to facilitate the passage of therapeutical molecules. The microneedle device has several advantages over conventional methods, such as better medication adherence, easiness and painless self-administration. Moreover, it is possible to deliver the molecules swiftly or over time. Microneedles can vary in shape, size and composition. The design process of a microneedle device must take in account several factors, like the location delivery, the material and manufacturing process. Microneedles have been used in a large number of fields from drug and vaccine application, cosmetics, therapy, diagnosis, tissue engineering, sample extraction, cancer research, wound healing, among others.

The popularity and growth of additive manufacturing(AM) in recent years are due to its ability to provide innovative solutions to complex manufacturing challenges and its potential to transform the way products are designed, developed, and produced. In this work, the numerical simulation geometry of the model extruder from nozzle is established along with the material extrusion process. The high gravity material extrusion system (HG-MEX) properties and characteristics during filament fabrication are evaluated. This work investigates the effects of gravity on the material extrusion process from nozzle extrusion. This work is unique in the incorporation of fluid surface characteristics to represent the performance and contact with the material extrusion in additive manufacturing. The use of simulation in high gravity material extrusion in additive manufacturing is a meaningful and valuable approach to improving the quality and efficiency of the process.

Metal fabrications experience complex physical metallurgical processes during additive manufacturing, leading to residual stress and coarse microstructure with directional growth. It significantly affects the comprehensive performance of the fabrications, which limits the application of additive manufacturing. Ultrasonic impact treatment (UIT), as a strengthening means to assist additive manufacturing, can effectively improve the stress state and refine the microstructure and the comprehensive performance. This paper introduces the effect of UIT on AM metal fabrications on microstructure morphology, stress distribution, surface roughness, internal defects, and comprehensive performance to gain a deeper understanding of the role of UIT on additively manufactured metal fabrications, which is based on the working principle and effect of process parameters. In addition, the strengthening mechanism of UIT in additive manufacturing is described from the perspective of surface plastic deformation and substructure formation, providing support for the shape and property control of metal fabrications in the process of additive manufacturing assisted by UIT. Finally, the issues that need to be studied in depth on UIT in additive manufacturing are summarized, and an outlook on future research directions is taken.

Literature review indicates that the basic microstructure of Ti-6Al-4V is bimodal which consist of two phases: α + β, and it occurs after the fabrication in conventional methods such as casting, plastic forming or machining processes. After the additive manufacturing methods, there could be significant changes in the microstructure. Due to the rapid heat exchange during heat-treatment process, bimodal microstructure transforms into lamellar microstructure which consists of two phases α′ + β. Despite applying optimum printing parameters, 3D-printed products exhibit typical surface defects and discontinuities. Recently, two of the primary technologies for the improvement of surface layer properties of titanium alloys are shot peening and electropolishing. Literature review signalizes that shot peening and electropolishing processes have positive impact on the corrosion behaviour, mechanical properties and the condition of the surface layer of titanium alloy. Furthermore, there is a lack of studies about combining shot peening and electropolishing in one hybrid process on titanium alloys, which could synthesize the benefits of both processes. The scope of future investigation is also included in this review.

In this research, a screw-barrel system was designed and implemented, coupled with material extrusion, to print high-melt-temperature thermoplastic materials. The aim was to integrate two methods: the direct extrusion of thermoplastic polymer pellets with material extrusion,utilize various polymers in the form of pellets, especially high melt temperature ones, instead of traditional filaments. This addresses one of the significant challenges in filament preparation for various materials that are not readily available in the market or nearly impossible to fabricate (such as highly filled plastics). First, the screw-barrel assembly performance and factors influencing feed rate were evaluated. Subsequently, specimens were printed with polyether ether ketone (PEEK), an advanced plastic material known for its outstanding mechanical properties, high-temperature tolerance, and broad applications in various sectors, such as medical. Additionally, the material was reinforced with 2.5% continuous glass fibers while printing, which presented a novel product. Upon comparing theoretical values with experimental data from the tensile testing, a notable observation emerged: the inclusion of 2.5% glass fibers led to an approximate 43% increase in the tensile strength of the material. Furthermore, SEM images provided visual evidence of satisfactory adhesion between layers and fibers.

Distributed Manufacturing (DM) is becoming increasingly important in operations management due to its potential to support sustainability goals, reduce risks in global supply chains, and boost local economies. However, previous analyses of the advantages and disadvantages of DM have mainly focused on operations, overlooking additional benefits across the supply chain. For example, DM can enable local sourcing, better serve end-users, and tackle reverse supply chain challenges. While economies of scale are essential for reducing costs and improving productivity, highly centralised manufacturing can increase transportation costs, vulnerability, and supply chain disruptions, particularly during pandemics or other times of restricted transportation. To address these challenges, this study introduces a Multi-attribute Decision Support System (MADSS) and assessment process that considers impacts across the supply chain and guides stakeholders, academics, and decision-makers. The MADSS handles quantitative and qualitative information, missing data, and uncertainty, and a team of experts from academia and industry in New Zealand has developed the evaluation. The MADSS was used to analyse DM and traditional construction alternatives from economic, social, environmental, and resilience perspectives in New Zealand. The research contributes to a better understanding of the impacts of DM across the entire supply chain. Also, it proposes a flexible decision-making framework to engage with stakeholders and support decision-making in other industries and regions.

Newcastle Disease (ND) remains a critical disease affecting poultry in sub-Saharan Africa. In some countries, repeated outbreaks have a major impact on local economies and food security. Recently, we developed an adenovirus-vectored vaccine encoding the Fusion protein from an Ethiopian isolate of Newcastle Disease Virus (NDV). The adenoviral vector was designed and a manufacturing process developed in the context of the Livestock Vaccine Innovation Fund initia-tive funded by the International Development Research Centre (IDRC) of Canada. The industrial-ly-relevant recombinant vaccine technology platform is being transferred to the National Veteri-nary Institute (Ethiopia) for veterinary applications. Here, we demonstrate that the instillation of the adenoviral vector through the nasal cavity can confer protection to chickens against a lethal challenge with NDV. A manufacturing process using HEK293 suspension cells cultured in stirred-tank bioreactor for the vaccine production is proposed. Taking into consideration supply chain limitations, options for serum-free media selection have been evaluated. A streamlined downstream process including a filtration, an ultrafiltration and a concentration step was de-veloped. With high volumetric yields (infectious titers up to 5 x 109 TCID50/mL) in the culture su-pernatant, the final formulations were prepared at 1010 TCID50/mL, either in liquid or lyophi-lized forms. The liquid formulation was suitable and safe for mucosal vaccination and was sta-ble for 1 week at 37˚C. Both liquid and lyophilized formulations were stable after 6 months of storage at 4˚C. Overall, a manufacturing process for adenovirus vectored vaccine was developed and protective doses were determined using a convenient route of delivery. Formulation and storage conditions were established, and quality control protocols were implemented.

Additive manufacturing (AM) or 3D printing of metals is gaining popularity due to its flexibility in fabricating parts with highly complex designs, as well as simplifying manufacturing steps and optimizing process times. In this investigation, 17-4 PH stainless steel was additively manufactured using Laser-Powder Bed Fusion (L-PBF), followed by functionalization through a DUPLEX treatment. This treatment involved a plasma-assisted nitriding process, followed by deposition of an arc-PVD c-Al0.7Cr0.3N hard coating. The microstructural modifications resulting from plasma nitriding (such as the formation of Fe2,3N and Fe4N, and the αN or expanded martensite phases), and the surface improvements with the c-Al0.7Cr0.3N coating on the 3D-printed 17-4 PH steel, are evaluated in comparison to conventionally manufactured 17-4 PH steel. These microstructural characteristics are correlated with the mechanical response of the treated surfaces. As a result of the plasma nitriding process, the hardness of the 3D-printed 17-4 PH SS increased by approximately 260%. The wear, measured through dynamic and static scratch-testing, was reduced by approximately 31%. This improvement was attributed to the modification of adhesive failure mechanisms, leading to a reduction in wear volume, improved coating adhesion, and enhanced scratch resistance.

During submerged laser peening using a pulsed laser, a bubble that behaves like cavitation is generated after laser ablation (LA). The bubble is referred to as laser cavitation (LC). The amplitude of the shock-wave in LA water is larger than that of LC; however, the impact passing through the target metal during LC is larger than that of LA. Thus, submerged laser peening is referred to as “laser cavitation peening”, as the peening method using the cavitation impact is known as “cavitation peening”. The impact induced by a hemispherical bubble is more aggressive than that of a spherical bubble with a microjet. Laser cavitation peening can improve the fatigue strength of metallic materials by producing work-hardening and the introduction of compressive residual stress. Three-dimensional additive manufactured metals (3D metals) such as titanium alloy are attractive materials for aviation components and medical implants; however, the fatigue strength of as-built components is nearly half that of bulk metals, and this is an obstacle for the applications of 3D metals. In the present study, published research papers were reviewed to identify the key factors of laser cavitation peening, with additional visualization of LC and data. Then, improvements in the fatigue strength of metallic materials, including 3D metals produced by laser cavitation peening, were summarized.

The information data accuracy affects the working reliability of production management system directly. Mobile equipment is an important part of integrated manufacturing system, in the course of its works, due to the complex working environment and the dynamic change of its position, it lead to the important problem of information detection distortion. Because the application of fractional differential operator has the dual functions of enhancing signal strength and reducing data error, we puts forward a data processing method of obtaining production information data of various accuracy by changing the parameters of fractional differential operator. Establish a mobile equipment detection data fusion processing model based on fractional order partial differential equations and apply it in the processing experiment of mobile equipment testing information detection data in an integrated manufacturing system, the feasibility and effectiveness of the method are verified. We conclude that the fractional order partial differential equations used in the processing of mobile equipment monitoring information data of an integrated manufacturing system has the function of obtaining production information data of various accuracy, which are of great significance to improve the working reliability in integrated manufacturing system. which is of great significance to improve the working reliability and scientific decision of integrated manufacturing system.