The paper delves into an extensive exploration of the integration of the circular economy paradigm within the realm of additive manufacturing (AM). The objective is to comprehensively investigate existing methodologies for structuring the symbiotic relationship between circular economy and additive manufacturing, while meticulously analyzing the current research gap concerning the implementation of circular economy principles in additive manufacturing practices. A thorough review focusing on the sustainability of additive manufacturing within the circular economy framework was conducted. This review aims to recognize and delineate pertinent aspects related to post-use material valuation in AM, recyclability of materials, and the environmental footprint associated with these processes. Emphasis was placed on the significance of examining circular economy facets concerning additive manufacturing processes to establish a holistic understanding. The overarching goal of this review is to augment knowledge regarding the potential advantages and benefits derived from the seamless integration of circular economy principles within AM. By elucidating the activities essential to attain compliance with the Sustainable Development Goals, this study endeavors to illuminate the pathway toward promoting sustainable development through the harmonious marriage of additive manufacturing and the circular economy.

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.

Background: Advances in additive manufacturing and the development of antimicrobial biopolymers facilitate the production of critical medical devices with strong and safe biocidal properties. The purpose of this study was to assess the antimicrobial efficacy, safety, and longevity of a polylactic acid-based biopolymer supplemented with a copper-based composite additive. Methods: An antimicrobial polylactic acid-based biopolymer was tested against several inoculants including Staphylococcus Aureus, MRSA, E. coli, Listeria, HCoV-229E (a SARS-CoV-2 surrogate), and HIV-1. Material safety was evaluated according to international testing standards for in vitro cytotoxicity. Results: The main findings of the present investigation showed a strong and long-lasting biocidal effect of a polylactic acid-based biopolymer embedded with a copper-based composite additive against Staphylococcus Aureus, MRSA, E. coli, Listeria, HCoV-229E (a SARS-CoV-2 surrogate), and HIV-1. Furthermore, the cytotoxicity and safety assessment of the antimicrobial biopolymer was found to be “non-toxic” and safe for human skin contact. Conclusions: The present investigation showed that the antimicrobial biopolymer exhibits strong and long-lasting biocidal properties against an array of viral and bacterial inoculants.

Additive manufactured interlocking structures often face challenges in achieving suitable joinability due to thermal deformation. This issue becomes particularly interesting when fabricating structures that require high density but not fully connected layers, such as soft-magnetic materials. This study focuses on systematical investigation of the deformation as a function of finger thickness and building direction in model interlocking structure. In the perpendicular direction to the build plate, the distortions of joint part were significantly reduced due to lower thermal stress. In addition, the effect of laser power and scan speed on interlocking structure are systematically discussed. Additionally, through stress and thermal simulations of additive manufacturing process, it was confirmed that the compensated model can enhance the joinability of interlocking structures. These findings contribute to the development of optimal design for additive manufactured parts of soft magnetic materials such as stators in electric motor components, which needs insulations and density.

The solved research problem is oriented to the implementation of advanced methods of component design and their sophisticated use in the ever-advancing additive manufacturing. Based on the stated objectives, the research was divided into several parts. Initially, the methods and principles of design in additive manufacturing were evaluated and investigated, from which the intent was subsequently defined and the DfAM method in the component design and development process was determined. The given design method, namely topology optimization (TO), was applied to multiple development and production cycles of additive manufacturing parts to validate the research conclusions. In this paper, the procedure for a specific part or component assembly is shown. By evaluating the developed design procedure using the TO method included in the design process, the conclusions are positive in terms of both time savings and properties of the designed part such as strength and weight parameters. The given findings need to be further developed by implementing on specific product lines for increasing the efficiency of the developed design methodology.

In Zambia, three feasibility studies have been conducted to assess the potential of wind energy for power generation. However, these studies did not investigate the capability of local support industries to manufacture wind turbine blades and towers. This study aimed to investigate and profile the capability of Zambian industries to manufacture wind turbine blades and towers. The study used a mixed-method approach to collect data; this utilized qualitative and quantitative approaches. The population of Zambian industries was collected from the Zambia Association of Manufacturers based on the registered members. This data was used to define the population for the study; the population was arranged into three strata whose characteristics are homogeneous within the stratum. Due to the low population, the study was conducted on the whole population. The qualitative data was collected from each stratum using a quantitative data collection tool developed for each stratum and analyzed. The study considered the capability of manufacturing companies based on wind potential results for Class III and Class IV, which exist in some sites. The results have revealed that on an as-is basis, no company in Zambia can manufacture WT blades and towers unless there is a complete overhaul of some companies or total investment in new infrastructure and equipment. The findings are useful for stakeholders involved in developing the wind power sector in Zambia.

The changes in consumer lifestyles have raised awareness of the variety of food options and packaging technologies. Active and smart packaging represents an innovative technology that serves to enhance the safety and quality of products. Smart packaging, as a subset of this technology, entails the integration of additives into packaging materials, thereby facilitating the preservation or extension of product quality and shelf life. This technological approach stimulates a heightened demand for safer food products with prolonged shelf life. Active packaging predominantly relies on the utilization of natural active substances. Therefore, the combination of active substances has a significant impact on the characteristics of active packaging particularly on polymeric blend like Polylactic acid (PLA) as a matrix. So, This review will summarize and explains how the addition of natural active agents influenced the properties related to the performance of active packaging through systematic analysis, providing new insights about the relationship between the types of active agents on physical-mechanical properties, bacterial colony reduction, and applications in foods packaging such as meat, fish, fruit, and vegetables. Through their integration, the market for active and smart packaging systems is expected to have a bright future.

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.

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.

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.

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.

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.

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.

Engine oil degradation and tribological properties are strongly interrelated. Hence, understanding the chemical processes resulting in additive depletion and degradation products is necessary. In this study, in-service engine oils from petrol and diesel vehicles were analyzed with conventional and advanced methods (mass spectrometry). Additionally, the effect of the utilization profile (short- vs. long-range) was studied. Petrol engine oils generally showed accelerated antioxidant and antiwear degradation and higher oxidation, especially in case of a short-range utilization profile, which can be attributed to the higher air-to-fuel ratio (more rich combustion) compared to diesel engines. A detailed overview of oxidation and nitration products, as well as degradation products resulting from zinc dialkyl dithiophosphate and boron ester antiwear additives, diphenylamine antioxidants and salicylate detergents is given. A side-reaction between oxidation products (aromatic carboxylic acids) and the boron ester antiwear is highlighted. This reaction was only detected in the petrol engine oils, where the oxidation products were measured in a high abundance. However, no side reaction was found in the samples from the diesel vehicle, since there the aromatic carboxylic acids were largely absent due to lower oxidation.

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.

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.

Skeletal muscle is a specialized tissue consisting of nondividing and multinucleated muscle fibers. Skeletal muscle-specific myostatin (MSTN) is a secreted protein that acts as a negative regulator of muscle mass by inhibiting proliferation and differentiation of myoblasts. In this study, we established a screening platform to monitor myostatin promoter activity in rat L8 myoblasts. Extract of Glycyrrhiza uralensis (GUE), an oriental herbal medicine, was identified through this screening platform and, the active fractions of GUE were identified by process-scale liquid column chromatography system. For in vivo study, the GUE as a feed additive was investigated in growth-finishing pigs. The results showed that GUE significantly increased body weight, carcass weight and lean content in pigs. Microbiota analysis indicated that GUE did not affect the composition of gut microbiota in pigs. In summary, this established rodent myoblast screening platform was used to identify a myogenesis-related phytogenic, GUE, and further demonstrated that the active fractions inhibited MSTN expression. These finding suggest a novel application for GUE in growth performance enhancement through modulation of MSTN expression. Furthermore, this established screening platform has great potential for identification and evaluation of a wide variety of phytogenics involving in myogenesis.

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.

In autonomous driving systems, high-speed and real-time image processing, along with object recognition, are crucial technologies. This paper builds upon the research achievements in in-dustrial item sorting systems and proposes an object recognition and sorting system for auton-omous driving. In industrial sorting lines, goods sorting robots often need to work at high speeds to efficiently sort large volumes of items. This poses a challenge to the robot's real-time vision and sorting capabilities, making it both practical and economically viable to implement a real-time and low-cost sorting system in a real-world industrial sorting line. Existing sorting systems have limitations such as high cost, high computing resource consumption, and high power consump-tion. These issues lead to the fact that existing sorting systems are typically used only in large industrial plants. In this paper, we design a high-speed, low-cost, low-resource-consumption FPGA (Field-Programmable Gate Array) based item sorting system that achieves similar perfor-mance to current mainstream sorting systems at a lower cost and consumption than existing sorting systems. The recognition part employs a morphological recognition method, which segments the image using a frame difference algorithm and then extracts the color and shape features of the items. To handle sorting, a six-degree-of-freedom robotic arm is introduced in the sorting segment. The improved cubic B-spline interpolation algorithm is employed to plan the motion trajectory and consequently control the robotic arm to execute the corresponding actions. Through a series of experiments, this system achieves an average recognition delay of 25.26ms, ensures smooth operation of the gripping motion trajectory, minimizes resource consumption, and reduces implementation costs.

(1) The study using the quails of the Manchurian golden breed was aimed at the efficacy of the food supplement based on micro-incapsulated phytase of Obessumbacterium proteus in the recombinant yeast producer Yarrowia lipolytica at a dose of 500 FYT/kg of the feed. (2) The effect of the supplement in the poultry diets on the productivity of young and adult quails was assayed; (3) It was found that the keeping the quails on the diets using the microencapsulated phytase O. proteus provided the highest live weight, the highest intensity of egg production, and the slaughter yield of animals, the lowest feed conversion compared to those in control groups and the groups getting a commercial phytase supplement based on Aspergillus ficuum. Taken together the data obtained in our experiments indicate a high degree of assimilation of phytate-containing products by the quails when using the encapsulated phytase compared to both the control and commercial preparation of phytase of the PhyA class. It can be concluded that it is a highly expedient and promising supplement based on the microencapsulated phytase of class D for feeding, which shows the best kinetic features compared to the phytases of PhyA and PhyC classes when working in the chyme of the poultry.

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.

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.

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 paper presents a new method of process parameters optimization, adequate for 3D printing of PLA (Polylactic Acid) components. The authors developed a new Hybrid Manufacturing Equipment (HME), suitable for producing complex parts made from a biodegradable thermoplastic polymer, for environmental sustainability. Our new HME equipment is producing PLA parts by both additive and subtractive techniques, with the aim of obtaining accurate PLA components with a good surface quality. A design of experiments has been applied for optimization purposes. The following manufacturing parameters were analyzed: rotation of the spindle, cutting depth, feed rate, layer thickness, nozzle speed, and surface roughness. Linear regression models and neural network models were developed to improve and predict the surface roughness of the manufactured parts. A new test part was designed and manufactured from PLA, to validate the new mathematical models, which can now be applied for producing complex parts made from polymer materials.

In this work, a systematic study was conducted on the fabrication of multi-material components obtained employing Laser-Powder Bed Fusion (L-PBF) technology. The idea of making multi-material components is a winning capability of additive technologies because it allows the fabrication of Functionally Graded Materials (FGMs) with the customization of parts according to different required properties. The transition from one material to another was achieved gradually and continuously within the same layer using ad-hoc equipment designed for L-PBF systems with a powder spreading technique based on coaters or rollers. The influence of the relative position of the different materials within the powder chamber and the geometry of the developed equipment on the metallurgical and mechanical properties of the manufactured samples was evaluated. The performed tests involved the use of two materials, a nickel-based superalloy, and a stainless steel, having different chemical, physical and mechanical properties to obtain gradual properties variation in the manufactured samples. Based on the results of post-process characterization obtained through metallographic, chemical, and mechanical analysis, the relative positions of the materials and the geometry of the developed equipment have a limited effect on the sample’s manufactured properties. The characteristics of the FGM zone depend on the nature of the employed powders, and its extent coincides with that defined during the design of the divider.

In this article, metallic additive manufacturing (AM) processes were classified and demonstrated. AM technology can be applied to a wide range of industrial areas because of its great feasibility in the design and manufacturing of various complex parts. The main factor that distinguishes various metallic AM processes from each other is the deposition method. Deposited metal can be imported in the form of melt or semi-solid. AM technology also covers a wide range of materials like aluminum, titanium, and many other alloys. Some of the common applications of metallic AM processes are the quick manufacturing of the parts which utilized in various industries like medical, automotive, aerospace, and jewelry industries. AM can help to fabricate parts that are impossible to be manufactured by other processes.

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.

In the era of Industry 4.0, the focus on optimizing manufacturing processes is crucial. This study provides a comprehensive analysis of the EnPAS system, a proprietary automated control and monitoring tool developed by KAD3, implemented in the production process of Corten steel furnishings - the "Lila" chair and "Line" planter. Designed to enhance operational efficiency, EnPAS facilitates improved workflow by streamlining production stages and reducing downtime. The research design involves a comparative analysis of the workshop's production process, both before and after the implementation of the EnPAS system. Elements such as de-greasing, oxidation, and finishing stages, among others, were examined. The implementation of the EnPAS system led to a significant reduction in the total production time for both products. Further, there was a substantial decrease in waiting time and setup time. These findings unequivocally indicate the transformative impact of the EnPAS system on the manufacturing process, showcasing its potential in driving efficiency improvements, reducing production times, and increasing output. The study underscores the importance of leveraging such advanced control and monitoring tools to optimize modern manufacturing processes and does not exaggerate conclusions not backed by the research.

Open-source 3-D printing has played a pivotal role in revolutionizing the additive manufacturing (AM) landscape, by making distributed manufacturing economic, democratizing access, and fostering far more rapid innovation than antiquated proprietary systems. Unfortunately, some 3-D printing manufacturing companies began deviating from open-source principles and violating licenses for the detriment of the community. To determine if a pattern has emerged of companies patenting clearly open-source innovations, this study presents three case studies from the three primary regions of open-source 3-D printing development (EU, U.S. and China) as well as three aspects of 3-D printing technology (AM materials, an open-source 3-D printer, and core open-source 3-D printing concepts used in most 3-D printers). The results of this review have shown that non-inventing entities called patent parasites are patenting open-source inventions already well-established in the open source community and in the most egregious cases commercialized by one (or several) firms at the time of the patent filing. Patent parasites are able to patent open-source innovations by using a different language, vague patent titles and broad claims that encompass enormous swaths of widely diffused open-source innovation space. This practice poses a severe threat to innovation and several approaches to irradicate the threat are discussed.

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.

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.

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.

Floating Offshore Wind Turbine (FOWT) is an innovative technology with little industry guidance for its hull design. Various FOWT floaters with different hull shapes claim to support the same turbines. Structural integrity and material expense analyses of different pontoon shapes were conducted, and it was found that some configurations, such as those with every two columns connected by both pontoon and bracing, have advantages over others. However, it is important to note that the choice of pontoon shape should be based on the wave loading conditions the floater will be exposed to. While a T-shaped pontoon provides a cost-effective solution under certain wave loading scenarios, it may not be the best option for all conditions. Specifically, ring pontoon designs with full bracing were found to be necessary for withstanding certain wave loads. Therefore, it is important to consider different Dominant Load Parameters (DLP) and ensure that a FOWT floater can withstand all applicable DLPs. An uneven hexahedral column shape, which combines the best attributes of square and round shapes, is proposed as a better alternative to cylindrical columns. It offers ease of manufacture and reasonably low drag. Bracing is found to be necessary for withstanding the wind turbine’s incurred moment and forces. The conclusion is that platform design should prioritize manufacturing costs and strength over maximizing hydrodynamic performance.

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.

In recent years, there has been a wave of research on complex networks. The purpose of this paper is to study the graph theoretic mathematical properties of random cyclooctatetraene chain. We obtain the explicit analytical expressions for the variances of the Gutman index, Schultz index, multiplicative degree-Kirchhoff index and additive degree-Kirchhoff index of a random cyclooctatetraene chain with $n$ octagons, which plays a crucial role in the research and application of topological indices.

This research focused on the perception of visually impaired individuals regarding tactile image creation using 3D printing. We developed and applied an experiment with 28 participants with visual impairments. They performed tasks based on skills whose foundations are needed and used in their daily lives, such as scanning, tactile discrimination, picture and spatial comprehension. The tactile images used in this study were developed by additive manufacturing and printed over 180 g/m² sulphite paper. The best performances were obtained when (1) the tactile image contour line heights were between 0.2 mm and 0.4 mm; and (2) the texture presented densities between 10% and 50%, when alone, or between 10% and 90%, when combined with another texture. The tested line widths (0.8 mm and 1.2 mm) did not present statistically significant differences in the results. Vision status (low vision or blindness) and moment of vision loss (congenital or late blind) did not influence the results. Schooling affected the activities of identifying objects and locating their copies. In the Conclusion section, there is a detailed recommendation list for the production and application of tactile materials, which range from simplifying forms to encouraging the use of both hands during content assimilation.

Artificial intelligence (AI) technologies, particularly the subfield of machine learning (ML), has been expected to bring significant benefits to all sectors of business and public services. The manufacturing industry is considered one of the domains most likely to benefit from AI tech-nologies. During the recent years, there has been a growing research and development effort on machine learning–based solutions for manufacturing industries, as shown by the growing number of research publications. However, the business, environmental or societal benefits of applying ML in manufacturing industry have not been reviewed and evaluated. This paper re-views the reported benefits of ML in the manufacturing industry by analysis of scientific publi-cations. The focus of the review is on research papers that were published from 2017 to 2021. The review pays particular attention to the industry sector, manufacturing operations, machine learning methods used, maturity of results and the benefits pursued and achieved. The benefits are divided into business benefits, environmental sustainability, and societal sustainability. The analysis shows that the maturity of ML-based solutions is still low, despite the intensive research. Consequently, future research projects should place more emphasis on the maturity of the solu-tions, which would lead to their broader adoption into operational use, thus benefiting industry and society at large. This calls for closer collaboration between the research community and in-dustry.

(1) Background. Cognitive impairment in people with Multiple Sclerosis (MS) has been studied in relation to some clinical variables (e.g., motor disability and disease duration) and lifestyle fac-tors such as Cognitive Reserve (CR). However, only very few studies have considered the interac-tion of clinical variables and cognitive reserve in preserving the integrity of the neuropsychologi-cal profile. In this paper, we hypothesised that a higher level of CR might predict good cognitive efficiency by modulating the clinical outcome of the disease. (2) Methods. A sample of 100 partic-ipants with MS (age range 30-74), was recruited and assessed remotely with a questionnaire to measure CR and a cognitive screening test. Data were analysed through Generalized Additive Models. (3) Results. We found that the model analysing the interaction between CR and disease duration, and between CR and motor disability was able to explain a significant percentage of cognitive performance. In particular, higher levels of CR predicted better cognitive performance despite long disease duration, unless the motor disability was severe. (4) Conclusion. This study highlights the crucial role of CR in modulating cognitive efficiency in people with MS.

In this paper, the characterization of 3D printed materials that are considered in the design of multirotor unmanned aerial vehicles (UAVs) for specialised purposes was carried out. The multirotor UAV system is briefly described, primarily from the aspect of system dynamics, considering that the airframe parts connect the UAV components, including the propulsion configuration, into a functional assembly. Three additive manufacturing (AM) technologies were discussed, and a brief overview was given of selective laser sintering (SLS), fused deposition modeling (FDM), and continuous fiber fabrication (CFF). Using hardware and related software, 12 series of specimens were produced which were experimentally tested utilizing a quasi-static uniaxial tensile test. The results of the experimental tests are given graphically with displacement-force characteristics. In this work, the focus is on CFF technology and the testing of materials that will be used in the production of mechanically loaded airframe parts of multirotor UAVs. Furthermore, an overview was given in such a way that the specimens were grouped, and the mean values of the maximum stress were presented, so that the tested materials could be more easily compared with conventional materials, such as aluminum alloys.

The deployment of a manufacturing execution system (MES) holds promising potential in facilitating the accumulation of a substantial amount of inspection data. Low quality levels in discrete manufacturing environments are the result of multi-factor coupling and failure to find quality issues in a timely manner within manufacturing settings may trigger the propagation of defects downstream. Currently, most of the inspection quality methods are direct measurement followed by manual judgment. The integration of deep learning methods provides a feasible way to identify defects in a timely manner, thus improving the acceptance rate of factories. This paper focuses on the design of a data-driven quality prediction and control model around discrete manufacturing characteristics, and use fuzzy theory to evaluate the quality level of production stages. Building Multivariate Long and short-term memory learning hidden quality representations to extract predictions from multi-level inspection data in manufacturing systems. Finally, by validating the data of actual produced water dispensers according to three evaluation indexes, RMSE, MAE, MAPE, the results show that Multivariate Long and short-term memory has better prediction performance.

HIV diagnosis and management have often caused disruptions to the everyday life and imagined futures of people living with HIV, both at individual and social levels. This has been conceptualised, in a rather dystopian way, as ‘biographical disruption’. This paper attempts to re-cast data from interviews with 40 men living with HIV (MLHIV) in Yogyakarta and Belu, Indonesia, in a slightly more positive light. Our analysis uses the concepts of additive and subtractive resilience strategies and we show how, rather than having a purely disrupted biography, participants talked about their experiences of ‘biographical reinvention'. Study participants were recruited using the snowball sampling technique, starting from two HIV clinics in the settings. Data were collected using one-on-one in-depth interviews, and a qualitative framework analysis was used to guide step-by-step data analysis. The findings showed that despite the disruptions in their everyday life (i.e., mental health condition, work, activities, social relationships, etc.) following the HIV diagnosis and management, MLHIV in our study managed to utilise their internal assets or traits (i.e., hope, optimism, resilience) and mobilised external resources (i.e., support from families, friends and healthcare professionals) to cope with the disruptions. An interweaving of these internal assets and external resources enabled them to take on new activities and roles (additive resilience strategies) and give up health compromising behaviours (subtractive resilience strategies). These were effective for most MLHIV in our study not only to cope with the HIV repercussions and improve their physical and mental health conditions, but to think or work on a ‘reinvented’ biography which encompassed resilience, hope and optimism for better health, life and future. The findings indicate the need for HIV interventions and healthcare systems that provide appropriate support for the development and maintenance of internal assets of PLHIV to enable them to cope with the repercussions of HIV and work on a ‘reinvented’ biography.

In recent years, in addition to the commonly known wire-based processes of Directed Energy Deposition using lasers, a process variant using the electron beam has also developed to industrial market maturity. The process offers particular potential for processing highly conductive, reflective or oxidation-prone materials. However, for industrial usage there is a lack of comprehensive data on performance, process limits and possible applications. The present study deals with this problem using the example of the high-strength aluminum bronze CuAl8Ni6. Multi-stage test welds are used to determine the physically possible process limits and draw conclusions about the suitability of the parameters for additive manufacturing. For this purpose, optimal ranges for energy input, possible welding speeds and the scalability of the process were investigated. Finally, additive test specimens in the form of cylinders and walls are produced and the hardness profile, microstructure and mechanical properties are investigated. It is found that the material CuAl8Ni6 can be well processed by wire electron beam additive manufacturing. The microstructure is similar to a cast structure, the hardness profile over the height of the specimens is constant and the tensile strength and elongation at fracture values achieved correspond to the specification of the raw material.

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.

Natural rocks are highly heterogeneous due to various geological processes that constantly alter their properties. The accumulation, deposition, and cementation of mineral and organic particles continuously modify the spatial characteristics of rock properties. Property variability or anisotropy is commonly observed in most rock types and influences strength, transport, and thermal conductivity behavior. This unpredictability presents a significant challenge for laboratory testing. Binder-jet additive manufacturing (3D printing) has emerged as a valuable technology for characterizing rock properties in geoscience and engineering. This study proposes a methodology to evaluate the variability and repeatability of mechanical properties of 3D-printed sandstones during binder-jet additive manufacturing. The mechanical properties were analyzed statistically for samples located in various parts of the 3D printer build volume. The results showed that the 3D-printed sandstones exhibited significant variations in their strength and stiffness properties when measured from samples produced within the same build volume during binder-jet additive manufacturing. The Uniaxial Compressive Strength (UCS) varied from 23 to 38 MPa, with an average value of 29 MPa. The Young's modulus, on the other hand, ranged from 1.5 to 4.05 GPa, with an average value of 2.33 GPa. The variability of the mechanical properties, quantified by the standard deviation, decreased when the entire population of 3D-printed sandstones was divided into smaller samples situated at different elevations of the build platform.

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.

This paper presents an overview of EREMI, a two-year project funded under ERASMUS+ KA203, and its results. The project team's main objective was to develop and validate an advanced interdisciplinary higher education curriculum, which includes lifelong learning components. The curriculum focuses on enhancing resource efficiency in manufacturing industries and optimizing poorly or non-digitized industrial physical infrastructure systems. The paper also discusses the results of the project, highlighting the successful achievement of its goals. EREMI effectively supports the transition to Industry 5.0 by preparing a common European pool of future experts. Through comprehensive research and collaboration, the project team has designed curriculum that equips students with the necessary skills and knowledge to thrive in the evolving manufacturing landscape. Furthermore, the paper explores the significance of EREMI's contributions to the field, emphasizing the importance of resource efficiency and system optimization in industrial settings. By addressing the challenges posed by under-digitized infrastructure, the project aims to drive sustainable and innovative practices in manufacturing. All five project partner organisations have been actively engaged in offering relevant educational content and framework for decentralised sustainable economic development in regional and national contexts through capacity building at local level. A crucial element of the added value is the new channel for feedback from students. The survey results, which are outlined in the paper, offer valuable insights gathered from students, contributing to the continuous improvement of the project.

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.

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.

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 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.

Polymeric composites such as Poly-ether-ether-ketone (PEEK)/carbon fiber (CF) have been widely utilized due to outstanding performances such as high specific strength and specific modulus. The PEEK/CF components via powder-bed fusion additive manufacturing usually show brittle fracture behaviors induced by their poor interfacial affinity and inner voids. These defects are strongly associated with powder packing quality upon deposition. The particle dynamic model has been widely employed to study the interactions of particle motions. Powder property, bulk material property, and interfacial features of composite powders are key factors in the particle dynamic model. In this work, an efficient and systematic material evaluation is developed for composite powders to investigate their deposition mechanism. The discrete element method is utilized to simulate the dynamic behaviors of PEEK/CF composite powders. The powder properties, bulk material properties, and interfacial features of powders are calibrated and justified by experimental measurement, numerical simulation, and design of experiments. The particle dynamic model can well explain the powder flow behaviors and interactions. It reveals that the addition of short CF particles can assist the flow of PEEK powders and improve the packing quality of the composite powders.

Biogas production from organic waste is a promising renewable energy source, but achieving optimal production and digester stability can be challenging. This study investigated the impact of the Evogen microbial additive on biogas production and digester quality through microbial abundance and physicochemical parameter analysis. Two biogas plants, BG01 and BG02, were examined using 16S rRNA profiling to assess microbial abundance. Simultaneously, physicochemical parameters, including FOS/TAC ratio, total solids, volatile solids, biogas production, and VFA profile, were measured to evaluate digester performance. Results revealed distinct microbial community shifts in Evogen-treated digesters. Increased abundance of methanogenic archaea and hydrolytic bacteria indicated improved anaerobic digestion. Evogen supplementation also positively affected digester performance, with higher FOS/TAC ratios indicating enhanced acidification and methanogenesis. Reductions in total solids and volatile solids demonstrated improved organic matter degradation. Significantly higher biogas production was observed in Evogen-treated digesters, highlighting its potential as a microbial additive. Furthermore, VFA profiling demonstrated improved process stability and reduced substrate inhibition in Evogen-treated digesters. In summary, Evogen microbial additive positively influenced microbial dynamics, improving biogas production and digester quality. These findings contribute to optimizing biogas production systems and understanding the complex microbial interactions within anaerobic digesters.

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.

In principle, modular or integral character of manufacturing lines depends on topological designs of products and determined operation tasks. On the other hand, in specific situations there is an articulated need for modular design in smart manufacturing systems, since modular layouts are a crucial step towards agile production via smart manufacturing. The aim of this paper is to explore how the modular layout relates to manufacturing lead time (MLT) and to operational complexity of smart manufacturing systems. For this purpose, topologically different models of alternative process layouts were simulated and tested, while MLT values were obtained using Tecnomatix Plant Simulation. Obtained positive findings of this research could be useful not only in selection of the most suitable process design from the alternative ones, but especially in deepening knowledge and better understanding of the concept of optimal network modularity.

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.

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 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.

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 article purposed to examine the impact of integration between Lean accounting (LA) tools & Just in Time (JIT) technique on Cost Reduction (CR) in modern manufacturing environment in Saudi establishments, these two variable moderated by Ethical Standards (ESs). A quantitative methodology was used for realizing the study objectives and answering study questions, the main tool exercised for producing the incipient data was a fully-structured questionnaire with closed ended questions, which was designed and passed around online among a selected sample of (109) managers and accountants in Saudi manufacturing firms. Descriptive and deductive statistics adopted to derive the findings, wherefore the compiled responses analyzed by SPSS & AMOS and the results showed that there is direct and indirect effect of LA tools & JIT technique on CR in Saudi industrial companies, especially when using ESs. These findings could provide decision makers in Saudi manufacturing companies with the acuity of adopting both methods in a combined way to maximize the profits of these firms. The results of this study also suggest that the application of LA tools & JIT technique has a positive effect on the promotion of sustainable manufacture and consequent achievement of high sustainable performance.

The rapid market changes and strong product individualization create the need for great flexibility of manufacturing management on new foundations, such as the Industry 4.0 model. Digital manufacturing is the basis for Industry 4.0, that have the following dimensions: (i) digital manufacturing based on advanced digital-oriented technologies, (ii) smart products (advanced manufacturing mode and new characteristics), and (iii) smart supply - chain (procurement of raw materials and delivery of finished products). Bidirectional exchange of information in collaborative manufacturing, using it exchange also for digital platforms of design of the innovative products. In this paper we are showing developed model of one Serbian digital factory model for Quality 4.0 (Q 4.0) , especially in workshop as a part of ERP/MES model.

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.

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.

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.

Nowadays there is a need to increase the density of interconnections on printed circuit boards (PCBs). Does this mean that the only way out for quality PCB manufacturing is to proportional increase in precision of equipment, or is there another way? One of the main constraints to increasing the density of PCB interconnections is the transition holes. As the number of conductive layers increases, the number of vias increases and they cover a significant space on the PCB. On the other hand, reducing the size of the vias is limited by the capability of spatial alignment of the PCB stack during manufacturing. There are standards that set limits for the design of contact pads on a PCB (IPC-A-600G, IPC-6012B). However, depending on the precision of production, the contact pads may be of poor quality. This raises the issue of determining the reliability of a contact pad with defined parameters at the design stage, taking into account manufacturing capabilities. This research proposes an analytical method for evaluation of reliability of a via or plated through-hole based on calculation of its probability of production in accordance with the current standards. On the basis of the method a model was developed both for the case of a contact pad without any conductors connected to it (nonfunctional contact pad) and for the real case with a connected conductor. The model estimates the probability of making an acceptable via for a given reliability class depending on parameters such as the conductor width (minimum permissible and usable), drilled hole diameter and pad diameter, as well as the accuracy of the drilling operation. The analysis of the modeling results showed that for the real case, a reduction in the reliability class would insignificantly affect the probability of making an acceptable via due to the tight limitation on the connection place of the conductor and the contact pad. In conclusion, we propose an algorithm for determining the optimal parameters of teardrops to minimize the negative impact of the conductor on the reliability of the vias.

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

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.

Abstract: Interactions in multicomponent Ni-Cr-Mo-Al-Re model alloy were determined by ab initio calculations in order to investigate the Re doping effect on Haynes 282 alloy. Simulation results provided an understanding of short-range interactions in the alloy and successfully pre-dicted the formation of a Cr and Re-rich phase. The Haynes 282 + 3 % wt. Re alloy was manu-factured using the additive manufacturing Direct Metal Laser Sintering (DMLS) technique, in which the presence of the (Cr17Re6)C6 carbide was confirmed by an XRD study. The results ob-tained provide useful information about the interactions between Ni, Cr, Mo, Al, and Re as a function of temperature. The 5-element model designed can lead to a better understanding of phenomena that occur during the manufacturing or heat treatment of modern, complex, multi-component Ni-based superalloys.

The paper investigates the influence of ion nitriding on the microstructure, corrosion resistance, and tensile strength at elevated temperatures of Haynes 282 nickel superalloy specimens produced by the Direct Metal Laser Sintering-DMLS technique. Analysis of the specimens’ surface morphology revealed that the ion nitriding process significantly affects the physical and chemical phenomena occurring on the surface of the specimens. Phase composition and stress analysis showed that Haynes 282 nickel alloy specimens produced by the DMLS technique and a chromium nitride layer produced in the ion nitriding process have tensile stress. The layer is also characterized by high nanohardness compared to specimens produced using the classical method (wrought) and DMLS technique. The conducted corrosion resistance tests confirmed the positive effect of the nitrided layer on the corrosion resistance of Haynes 282 nickel alloy produced by the DMLS technique. The ion nitriding process did not significantly affect the elevated-temperature tensile strength of Haynes 282 nickel alloy specimens produced by the DMLS technique.

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.

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.

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.

Once on Mars, maintenance and repair will be crucial for humans as supply chains including Earth andMars will be very complex. Consequently, the raw material available on mars must be processed and used. Factors such as the energy available for material production play just as important a role as the quality of the material that can be produced and the quality of its surface. With the aim of developing and technically implementing a process chain that meets the challenge of producing spare parts from oxygen-reduced Mars regolith, this paper addresses the issue of low-energy handling. Expected statistically distributed high roughnesses of sintered regolith analogs are approximated in this work by parameter variation in the PBF-LB/M process. For low-energy handling, a dry-adhesive microstructure is used. Investigations are carried out to determine the extent to which the rough surface resulting from the manufacturing process can be smoothed by deep rolling in such a way that the microstructure adheres and enables samples to be transported. For the investigated AlSi10Mg samples (12 mm x 12 mm x 10 mm), the surface roughness varies in a wide range from Sa 7.7 µm to Sa 64 µm after the additive manufacturing process, pull-off stresses of up to 6.99 N/cm² could be realized after deep rolling. This represents an increase in pull-off stresses by a factor of 392.94 compared to the pull-off stresses before deep rolling, enabling handling of even larger specimens. It is noteworthy that specimens with roughness values that were previously difficult to handle can be treated post-deep rolling, indicating a potential influence of additional variables that describe roughness or ripples and are associated with the adhesion effect of the microstructure of the dry adhesive.

To obtain a given structure in 3D building-up modes, it is necessary to use optimal surfacing modes that determine the amount of heat input in local areas. For this purpose, aluminum bronze was surfacing onto a deformed base and the structure and its characteristics in the surfacing and heat-affected zones were studied by the EBSD method. The heterogeneity of the formation of the structure in each selected zone is established, which indicates the heterogeneity of heat input in local areas of the material in one mode of surfacing. For typical cases of crystallization, a molecular dynamics simulation of crystallization processes with different heat input to the base with characteristics specified based on experimental data was carried out. It has been established that the amount of heat input determines the degree of melting and the inherited defectiveness of growing crystals. The formation of misorientation boundaries and crystallization centers of new grains is determined by the conditions of joint growth of grains with given crystallographic parameters of the computational model. Numerical calculations agree with the experimentally observed results.

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.

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.

The presented research article shows a functionally graded lattice as an infill structure in designing a 3D printed mechanical element—a bicycle crank arm. The authors want to answer the fundamental question: whether the functionally graded lattice structures can efficiently improve structural performance and how they can be implemented in real complex components. Intuitively, everyone senses that using such elements must bring profit. A good example might be nature, which commonly reaches for such a solution in the skeletons of animals and humans. Two aspects determine their realisations: the lack of adequate construction and analysis methods and the limitations of existing manufacturing methods. Thus, the authors used relatively simple crank arm design and design exploration methods for structural analysis. The design exploration method enabled us to find the optimum solution efficiently. A developed prototype was created utilising additive manufacturing. One of the 3D printing methods - fused filament fabrication technology for metals, allowed the prototype of a crank arm with the optimised infill to be made. As a result, the authors developed a lightweight and manufacturable crank arm showing the new construction and analysis method implementable in similar 3D printed elements.

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.

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.

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.

Vision-based tactile sensors (VBTS) have become the de facto method of giving robots the ability to obtain tactile feedback from their environment. Unlike other solutions to tactile sensing, VBTS offers high spatial resolution feedback without compromising on instrumentation costs or incurring additional maintenance expenses. However, conventional cameras used in VBTS have a fixed update rate and output redundant data, leading to computational overhead downstream. In this work, we present a neuromorphic vision-based tactile sensor (N-VBTS) that employs observations from an event-based camera for contact angle prediction. Particularly, we design and develop a novel graph neural network, dubbed TactiGraph, that asynchronously operates on graphs constructed from raw N-VBTS streams exploiting their spatiotemporal correlations to perform predictions. Although conventional VBTS uses an internal illumination source, TactiGraph is reported to perform efficiently in both scenarios, with and without an internal illumination source. Rigorous experimental results revealed that TactiGraph achieved a mean absolute error of 0.62∘ in predicting the contact angle and was faster and more efficient than both conventional VBTS and other N-VBTS, with lower instrumentation costs. Specifically, N-VBTS requires only 5.5% of the compute-time needed by VBTS when both are tested on the same scenario.

Rapid solidification during metal additive manufacturing (AM) leads to non-equilibrium microsegregation, which can result in the formation of detrimental phases and cracking. Most of the microsegregation models, assume a Scheil-type solidification, where the solidification interface is planar and there exists local equilibrium at the interface along with either zero or infinite solute diffusion in the respective participating phases - solid and liquid. This assumption leads to errors in prediction. One has to account for finite solute diffusion and the curvature at the dendritic tip for more accurate predictions. In this work, we compare different microsegregation models that do and do not consider finite diffusion and dendrite tip kinetics against the experiments. We also propose a method to couple dendrite tip kinetics with the diffusion module (DICTRA®) implemented in Thermo-Calc®. The models which accounted for both finite diffusion and dendrite tip kinetics matched well with the experimental data.

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.

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.

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.

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.

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.

In this age of global warming, academics and policymakers are increasingly concerned about firm environmental sustainability success. Therefore, this study aims to investigate whether Environmental, Social and Governance (ESG) performance impacts sustainability performance through the mediating effect of firm innovation. To this end, Structural Equation Modeling (SEM) was deployed to analyze data collected from the employees of manufacturing industries in an emerging economy like Bangladesh. The results revealed that ESG performance significantly enhances the innovation and sustainability performance of manufacturing industries, indicating that the higher the ESG performance of a firm, the greater its innovation and sustainability performance. Furthermore, the results confirmed that firm innovation performance fully mediates the relationship between ESG initiatives and sustainability performance. The findings of this study provide policymakers and industry authorities with valuable insight into the role of ESG and innovation performance in improving sustainability performance. Specifically, the study sheds knowledge on how firm ESG initiatives and innovation performance impact sustainability performance in the manufacturing sector of an emerging economy like Bangladesh.

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.

Metal additive manufacturing technologies have large potential for future use in load bearing aerospace applications, requiring a deeper understanding of mechanical performance and influencing factors. The objective of this study is to manufacture fatigue specimens by laser powder bed fusion with comparable bulk and different as-built surface quality (without post-processing). This goal was pursued by using identical bulk/hatch scan parameters while varying contour scan parameters. The bulk quality was evaluated by density measurements according to Archimedes’ principle and tensile testing. The surfaces were investigated using the optical fringe projection method and surface quality was assessed by the areal surface texture parameters Sa (arithmetic mean height) and Sk (core height, derived from material ratio curve). The resulting mean values of max (ultimate tensile strength) were between 375 and 405 MPa, which is a variation of roughly 8%, for seven different surface conditions. It could be confirmed that the influence of contour scan variation on bulk quality is insignificant for the assessed samples.

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.

Research has shown that innovativeness is a key factor leading to company performance. However, the business environment can play a detrimental effect on company performance by undermining the company’s internal strategic efforts in terms of innovativeness. To test this, 344 questionnaires were sent to manufacturing companies owners and managers. 178 of them were returned and usable to test the study hypotheses. Though a hierarchical regression analysis, it was found that innovativeness has positive and significant effect on company performance, but business environmental dynamism has a negative and significant effect in innovativeness-company performance relationship. Recommendations and other research perspectives are suggested

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 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.

Design for Manufacture and Assembly (DfMA) in architectural, engineering, and construction (AEC) industry is attracting the attention of designers, practitioners, and construction project stakeholders. Digital fabrication (Dfab) and design for additive manufacturing (DfAM) practices are found in current needs for further research and development. The DfMA's conceptual function is to maximize the process efficiency of Dfab and AM building projects. This work reviewed 171 relevant research articles over the past few decades. The concept of DfMA and the fundamentals of DfMA in building and construction were explored. In addition, DfMA procedures associated with Dfab and DfAM, as well as its AM assembly process, were discussed. Lastly, the current machine learning research on DfMA in construction was also highlighted. Large research gaps in the DfMA for Dfab and DfAM can be filled to increase operational efficiency and sustainable practices.

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.

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.

Rapidly providing a definitive diagnostic test that can be used broadly by healthcare providers and members of the public in the setting of a disease emergency is critical to limit pathogen spread, develop and deploy medical countermeasures, and mitigate the social and economic harms of a serious epidemic or pandemic. There is extraordinary expertise within and outside of government working on these issues, and major accomplishments have been made to accelerate test development, expand laboratory testing capacity, and establish widespread point-of-care testing. Still, the United States does not have a plan to rapidly respond, to develop, manufacture, or deploy at national scale diagnostic testing in the earliest days of a new infectious disease crisis. Nor does the nation have a plan to sustain testing capacity at high volume over the course ofan enduring epidemic or pandemic. To address this gap, we are proposing a National Diagnostics Action Plan that describes the steps that are urgently needed to prepare for future infectious disease emergencies, as well as the actions we must take at the first signs of such events. These recommendations require substantial collaboration between the US government (USG) and the private sector to solve a series of challenges now, as well as to prepare for the massive and rapid scale-up of laboratory and point-of-care test development and testing capacity in future emergencies. The recommendations include establishing pre-event contracts; ensuring rapid access to clinical samples; creating a permanent public-private testing coordinating body to allow for rapid information sharing and improved cooperation among the USG, test developers, and clinical laboratories; and accelerating testing rollout at the beginning of an event—andthus, the effective public health management of a disease crisis. These recommendations were informed by extensive discussion with people who managed the COVID-19 and monkeypox responses, review of past reports written on diagnostic challenges, and the experiences of the authors.

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.

Laser Powder Directed Energy Deposition (LP-DED) is an Additive Manufacturing process in which a laser beam is used to generate a melt pool onto a substrate. The additional material, in form of powder material, is fed into the generated melt pool and a raised track is obtained. The LP-DED process is very powerful for different applications such as the repair operations and the production of functionally graded material, however the application is still limited and one of the main reasons is related to the lack of knowledge in the physics of the process mechanisms. The process is influenced by a huge number of parameters and three main mechanisms can be identi-fied. These mechanisms are the powder flow, the generation of the molten pool and the solidifica-tion process and for each of these mechanisms the process parameters have a different relevance. In this paper, a review of the main mechanisms and the relationship between the process parame-ters and the outcome of each step of the process is presented.