Engineering

Abstract: The integration of advanced materials, additive manufacturing processes, and simulation-driven design is becoming increasingly important in the development of high-frequency electronic and machinery components. In this work, a simulation-driven design methodology is presented for gold nanoparticles-based (AuNPs) conductive structures intended for implementation on low-temperature co-fired ceramic (LTCC) substrates. The approach incorporates both electromagnetic performance requirements and manufacturing constraints associated with already developed printed conductive materials. As a representative application, a microstrip coupled-line band-pass filter operating in the Ku-band frequency range is designed using full-wave electromagnetic simulations. The geometry is optimized while considering realistic limitations related to conductor thickness, minimum feature size, and effective electrical conductivity of AuNPs based conductive layers. The AuNPs are produced via ultrasonic spray pyrolysis (USP), liophilizied, dispered in a solvent, plasma printed on a Al2O3 substrate and sintered. The resulting properties thus deviate from bulk gold. The optimized structure of the AuNPs LTCC filter exhibits a center frequency of approximately 15 GHz with a bandwidth of about 1 GHz. Simulated S-parameter results demonstrate efficient signal transmission within the passband and strong attenuation outside the operating frequency range.

Abstract: Photovoltaic (PV) backsheet polymers degrade over time when exposed to the UV, especially the loss of tensile strength in multilayer polyethylene terephthalate/ethylene vinyl acetate (PET/EVA). The traditional OLS and WLS regressions do not account for the regime dependence and nonlinearity of the kinetics. The 511 daily field measurements of a commercial PET/PET/EVA backsheet were used in this study to fit a Generalized Additive Model (GAM) with penalized thin-plate regression splines and ridge regularization. The GAM significantly outperformed benchmarks: in-sample R² = 0.9978 and RMSE = 0.80 MPa, compared to OLS (R² = 0.9249, RMSE = 4.69 MPa) and WLS (R² = 0.9202, RMSE = 4.84 MPa). The residual variance analysis revealed that the residual variance scaled with the cumulative UV dose (α = 0.5917, p < 0.001) and varied by 13.4-fold. A key finding was biphasic degradation: 0.152 MPa/(MJ/m²) in Phase 1 (UV < 196.53 MJ/m²), accelerating 3.79× to 0.575 MPa/(MJ/m²) in Phase 2. Over 511 days, tensile strength dropped 73.17 MPa (30.2% loss from 241.93 MPa initial). The results show that GAMs are a better method for modeling the reliability of PV backsheets.

Abstract: Fiber-reinforced polymers such as basalt fiber-reinforced polymers (BFRP) can be used in structural parts, which often require assembly operations. Thus, the surface quality after drilling operations is especially important. BFRP laminates have been drilled with three different tools, and their profile roughness and surface roughness have been evaluated by analyzing the following variables: average roughness (Ra), maximum height of profile (Rz), arithmetic mean height (Sa) and maximum height (Sz), by means of an optical system. The optical measurement of surface roughness has been hampered by fiber breakage. A statistical analysis has allowed developing a general linear model that predicts the values of variables. The fitted model for Ra and Rz has a variation coefficient of 97.00% and 95.58% respectively, while that 91.74% and 68.02% for Sa at the inlet hole and outlet hole respectively; and 86.08% and 82.22% for Sz at the inlet hole and outlet hole respectively. Additionally, different Machine Learner for regression algorithms have been applied using different configurations to establish prediction models of the main rugosity parameters. In this way, Linear methods, Gaussian Regression methods, Support Vector Machines, and Fine Trees, have been applied using as features the rotation speed, feed rates, and tool. Also, a Neural Network has been optimized and applied for the same goal. The methods have yielded satisfactory prediction results for some roughness parameters. Although the type of drill bit, but the behavior of all variables is similar for all the drill bits, those with point angle 120° provide better results.

Abstract: Digitalization of fitting processes represents an important direction of production development in mechanical engineering in the context of Industry 4.0, smart manufacturing, and human-centered manufacturing. In manual assembly operations, the quality of the result is significantly affected by the comprehensibility of work instructions, capability of the user to interpret technical documentation and degree of prior experience. The present paper deals with an experimental comparison of traditional paper documentation with a digital workflow created in the technology of Vuforia Capture. The aim was to verify whether digital work instructions can contribute to reduction of assembly time, reduction in error rates, and reduction in the need for additional assistance with installing an unfamiliar mechanism. The experiment was conducted on a group of employees who had not been familiar with the mechanism to be assembled and did not have practical experience with the same prior to testing. The assembly task was performed in two versions: using paper documentation vs. following a supporting digital procedure in Vuforia Capture. Total assembly time, the number and type of errors, the need for assistance, the comprehensibility of the procedure, and the applicability of the technology to the conditions of engineering practice were evaluated. The results showed that in the case of paper documentation, the average assembly time was 1084.8 s, while in the case of the digital procedure, the average time was 599.4 s. The total number of errors recorded decreased from 56 to 9, and the number of assistance interventions from 29 to 1. Findings suggest that Vuforia Capture can foster a more effective understanding of assembly procedures, stabilize workflow, and accelerate training of inexperienced users.

Abstract: This study presents a hydrogeochemical assessment of formation waters from the Karazhanbas, Zhetybay, and Uzen oil fields in the Mangystau Region of Kazakhstan, with the objective of elucidating the distribution of lithium, identifying the geochemical factors governing its accumulation, and evaluating its extraction potential. The investigation considered key physicochemical parameters, ionic composition, lithium concentrations, and the relationships between Li, total dissolved solids (TDS), pH, and major cations. Major ions were determined by standard hydrochemical methods, while lithium concentrations were analyzed via inductively coupled plasma optical emission spectrometry (ICP-OES). Results indicate that all investigated waters belong primarily to the calcium-chloride type according to Sulin’s classification. In terms of TDS, the sequence of increasing salinity is Uzen > Zhetybay > Karazhanbas. Specifically, TDS values for Karazhanbas range from 3,725.9 to 40,891.3 mg/dm³, for Zhetybay from 110,800.1 to 150,104.8 mg/dm³, and for Uzen from 130,387.4 to 163,107.1 mg/dm³. Lithium content varies from 0.30-0.70 mg/dm³ in Karazhanbas waters to 1.40-1.85 mg/dm³ in Zhetybay, while Uzen waters reach concentrations of 1.51 mg/dm³. Positive correlations were established between Li and Ca²⁺, Mg²⁺, and Na⁺+K⁺, suggesting that lithium accumulation is linked to the overall salt matrix and water-rock interaction processes. The data demonstrate that maximum mineralization does not always correspond to maximum lithium content. Zhetybay is identified as the most promising site for further lithium-oriented research. The formation waters of the Mangystau Region may be considered a potential secondary hydromineral resource; however, their industrial feasibility requires further geochemical, technological, and environmental evaluation.

Abstract: The Job Shop Scheduling Problem (JSSP) is a paradigmatic and strongly NP-hard combinatorial optimisation problem that underpins production planning in modern manufacturing systems, and constraint programming (CP) has become one of the leading methodologies for tackling it. However, comparative studies of CP solvers for the JSSP have so far been restricted to a single benchmark family, a single instance-size range, or a single hardware setting, which limits the practical guidance they offer to both researchers and practitioners. This paper presents a controlled empirical evaluation of four state-of-the-art CP solvers—IBM ILOG CP Optimizer, Google OR-Tools (CP-SAT), Hexaly, and OptalCP—on the makespan-minimisation JSSP. The four engines are run with default parameters and a uniform 600-second wall-clock time budget on 332 instances drawn from nine canonical benchmark families (Fisher–Thompson, Lawrence, Adams–Balas–Zawack, Applegate–Cook, Yamada–Nakano, Storer–Wu–Vaccari, Taillard, Demirkol–Mehta–Uzsoy, and Da Col–Teppan), spanning sizes from 6 × 5 up to 1000 × 1000 operations. OptalCP emerges as the most robust engine overall, certifying optimality on 57.5% of the instances with the smallest average optimality gap (3.55%), while Hexaly dominates on industrial-scale problems and produces the bulk of 31 new best-known upper bounds and one new best-known lower bound reported here. Solver competitiveness depends sharply on instance size and on the n/m ratio, with square instances confirmed as the hardest case. These findings support an instance-aware approach to CP solver selection in industrial scheduling.

Abstract: Nowadays, the organisational landscape aiming to provide value through their product and service offerings relies on having the infrastructure necessary to deliver at the expected service levels, as well as contributing to business continuity and organisational resilience in the face of modern organisational performance disruptions. This requires appropriate adaptation of existing frameworks, methods, and models to their business models which have generated consistent deliverables across time and industries. The same is applicable for the Romanian Information Technology (IT) organisations, which face increasing pressure to deliver within the expected quality, time, and budget parameters. Therefore, this paper aims to assess how stakeholder relationship management components, viewed through the Malcolm Baldrige National Quality Award (MBNQA) excellence framework, with impact on organisational quality and its contribution to business continuity and organisational resilience in Romanian IT organisations. This is a pilot-type study with a sample of N = 52 participants, to explore the applicability of the MBNQA framework within the Romanian IT sector. The results suggest that the four components of MBNQA focused on stakeholders (Leadership and Governance, Workforce, Customers and Markets, Community Engagement) may be suitable to be considered in assessment tools on the Romanian IT market. The “Workforce” variable emerges as the strongest area to focus on for achieving quality in stakeholder relationship management (SRM). Given its pilot delimitation, this study provides can be seen as providing an initial foundation for applying MBNQA in a specific regional IT context. While limited by sample size and geographic focus, the findings justify expanding the research to include broader population segments. Future research could transition from this correlational design to longitudinal frameworks to validate the associations across other multiple geographical markets.

Abstract: Friction stir welding (FSW) began as a fairly specialized joining method, but over the past three decades it has evolved into something considerably more versatile, a manufacturing platform that now handles complex multi-material assemblies and solid-state additive processes with reasonable reliability. This review follows this evolution, paying particular attention to friction stir additive manufacturing (FSAM) and the persistent difficulties that arise when joining dissimilar systems: aluminum to steel or metals to polymers, where the fate of the joint is largely decided by how well the intermetallic compounds are kept under control. Machine learning, artificial intelligence, and high-fidelity numerical models are reducing the reliance on trial-and-error that once dominated parameter selection and defect prediction, bringing FSW closer to the operating principles of Industry 4.0. Hybrid variants, including ultrasonically assisted and underwater FSW, are also receiving attention here, as they offer researchers finer control over heat generation and plastic flow than the standard process allows. Throughout the study, microstructural observations are directly connected to mechanical results, with the aim of analyzing the current state of solid-state manufacturing and identifying the questions that most urgently need answering.

Abstract: Additive manufacturing is moving towards the use of machines with five or more axes but is limited by the inability to easily generate print paths. This usually requires the creation of custom G-code in order to utilize five-axis printing. However, as additive manufacturing begins to utilize five-axis printing for scenarios such as repair, modification, or composite printing, the existing print surfaces become potential obstacles that need to be accounted for in path planning. This paper shows a novel way of expanding the capabilities for printing on complex, concave parts, in order to prevent collisions between the printer and the part. This is achieved through two main steps: Area Refinement, and Angle Determination. During Area refinement, the proposed print area is altered based on the geometry of the print surface, along with printer parameters. This results in a print region that is feasible with the given machine configuration and geometry, that will not attempt to print too close to any existing surface in the presence of concavities. During Angle Determination, the finalized print paths are adjusted to set a nozzle orientation that prevents the machine from colliding with the part. In this paper, we present the algorithmic details behind these approaches and show computational results for two complex concave geometries.

Abstract: Trends in Micro- and Nano-Lithography required for future development of large area applications ranging from high-packing-density electronics to solar cells are surveyed and outlined. Strategies to use direct laser writing to define etch masks over large areas by: i) fixed beam moving stage and ii) moving beam moving stage approaches are presented. The extension of planar 2D and stacked 2D (or 2.5D) fabrication methods into 3D micro- and nano-fabrication is discussed. One of the essential future characteristic of 3D nanolithography is real-time feedback capability. This can be realised via inherent 3D-capable holography, which bridges lithographic exposure control, wavefront sensing, and adaptive feedback, providing a pathway to stitch free, large area 3D patterning. The future of micro-fabrication is expected to evolve via highly specialised 3D architecture design and reduction of post-processing steps.

Abstract: The transition toward a circular economy (CE) in the plastic recycling sector requires integrated management frameworks that align technical performance with organizational governance. This study proposes an exploratory diagnostic framework for formalized recycling SMEs, integrating Latent Dirichlet Allocation (LDA) and Random Forest (RF) algorithms. Given the specialized nature of the sector, a purposive sample of 16 ‘pioneer’ SMEs in Bogotá was analyzed. Data were standardized through a 5-point ordinal scale, and the Spearman rank correlation analysis (ρ≥0.85) revealed high internal consistency and structural synchronization. This high correlation reflects the operational homogeneity of the analyzed vanguard rather than a universal statistical generalization. The findings suggest that for these leading firms, circularity is driven by social impact, collaborative networks, and systemic process reengineering. The proposed framework serves as a methodological blueprint for analytical generalization, providing an adaptable diagnostic tool that can be iteratively refined as the sector matures and data availability increases.

Abstract: Water hyacinth (Eichhornia crassipes) is an invasive aquatic plant with high lignocellulosic content, offering potential as a natural fiber resource for craft-based industries. However, its extremely high initial moisture content (≈95%) presents a major challenge in fiber processing, particularly for small-scale industries that rely on traditional sun-drying methods. These methods are highly dependent on weather conditions, prone to contamination, and produce inconsistent fiber quality. This study adopts a research and development (R&amp;D) approach to design and evaluate an innovative dryer machine specifically for water hyacinth fiber processing. The proposed system utilizes LPG-based heating and controlled airflow to achieve stable drying conditions. Experimental results show that the dryer machine can process 10 kg of wet water hyacinth within 280 minutes, significantly shorter than approximately four days required for manual drying. The system reduces the moisture content to below 10%, resulting in improved fiber cleanliness, uniformity, and usability. Although the dried mass produced by the machine is slightly lower compared to manual drying, this is attributed to more effective moisture removal, leading to lower residual water content in the final product. Productivity analysis indicates improved operational consistency and higher processing capacity over extended periods (30–180 days), particularly under varying weather conditions. These findings demonstrate that controlled drying technology provides a reliable and efficient solution for lignocellulosic fiber processing in small-scale industries, contributing to improved material utilization and sustainable biomass management.

Abstract: This study examines how the Internet of Things (IoT) acts as a key enabler of sustainability in industrial production systems within the Industry 4.0 paradigm, addressing the fragmented understanding of the mechanisms linking digital technologies to environmental, operational, and emerging human-centric outcomes. A systematic literature review was conducted following PRISMA 2020 guidelines using the Web of Science Core Collection. After applying explicit inclusion and exclusion criteria, 69 peer-reviewed studies published between 2016 and 2026 were analyzed through qualitative thematic synthesis and comparative analysis. The findings reveal that IoT functions as a foundational digital infrastructure enabling real-time monitoring, operational transparency, and data-driven decision-making in production environments. Four dominant application domains are identified: (i) energy and resource efficiency, (ii) production monitoring and control, (iii) predictive maintenance and asset management, and (iv) emerging human-centric production systems aligned with Industry 5.0. While IoT consistently improves operational reliability and resource efficiency, its contribution to the social dimension of sustainability remains comparatively underdeveloped. This study advances existing literature by providing a mechanism-oriented synthesis that explains how IoT-enabled infrastructures generate sustainability outcomes across production systems. Furthermore, it establishes a conceptual bridge between Industry 4.0 digitalization and the transition toward human-centric and resilient manufacturing models associated with Industry 5.0. From a practical perspective, the results highlight that IoT adoption contributes to reducing energy consumption, optimizing resource utilization, and enhancing operational performance, while also supporting safer and more adaptive working environments. However, challenges related to data integration, workforce adaptation, and digital capability gaps persist, underscoring the need for inclusive and strategically aligned digital transformation processes.

Abstract: The objective of this research was to develop an intelligent stress mapping and a smart control platform, utilizing Artificial Intelligence (AI), to increase the fatigue life of a hydraulic crane. The crane's boom was modeled and co-simulated using ANSYS, ADAMS, and MATLAB. A flexible model of the boom was created in ANSYS and then exported to ADAMS. Stress analysis was performed using the maximum principal hotspot method and the von Mises yield criterion. Stress optimization was conducted using a Neural Network (NN) algorithm, which is a key implementation of AI in this study. Two control platforms, one based on Neural Networks and another on Fuzzy Logic, were designed to apply AI in controlling the crane's movements. The Neural Network algorithm optimized the crane's movement by adjusting velocity at critical positions where structural stress was high, while the fuzzy logic-based control algorithm utilized stress feedback from the crane's structure. Both AI-driven control algorithms were integrated into the physical crane in the lab, and extensive testing demonstrated a significant increase in the crane's fatigue life, along with effective damping of crane vibrations. This paper introduces a novel AI-driven approach combining Neural Networks and Fuzzy Logic for intelligent stress mapping and control, specifically tailored for hydraulic cranes. Unlike previous works, this research integrates real-time stress feedback into the control process and validates the algorithms through experimental implementation on a prototype crane, significantly improving its fatigue life.

Abstract: Data science techniques are increasingly employed to enhance process efficiency, reduce energy consumption and operational costs, enable active process control, ensure consistent product quality, and support predictive maintenance in modern manufacturing systems. A central question arising from recent developments is: How can data models fundamentally transform manufacturing processes, and what are the primary barriers to their widespread adoption? Contemporary manufacturing sectors are progressively integrating data models within digital twin and digital shadow frameworks to enable real-time process optimization and data-informed decision-making. However, the inherent complexity of manufacturing processes—combined with the frequent scarcity of high-quality, balanced datasets—often limits the generalizability and interpretability of purely data-driven models. In practice, quality, contextual relevance, representativeness, and richness of data are significantly more critical than its sheer volume when developing robust and reliable models. This paper provides a comprehensive overview of the application of data modeling in dynamic manufacturing environments. It examines key aspects such as data generation, sampling strategies, data preprocessing and handling, and model development methodologies across steady-state, transient, and generative process regimes.

Abstract: The production of sugar from sugarcane cultivation was one of the most significant industrial activities in eastern Andalusia during the late nineteenth and early twentieth centuries. Along the Málaga coastline, remnants of these sugar mills can still be found, many of them currently in a state of abandonment or poor conservation. This study presents the 3D digital reconstruction of the San Joaquín sugar mill (Maro, Málaga, Spain) using UAV-based photogrammetry. A point cloud was generated from aerial images processed with specialized software. A comparative analysis was conducted using between 12.5% and 100% of the captured images to evaluate model accuracy and computational cost. The full dataset (100%) produced the most complete and accurate model, although at a higher computational cost. Reduced datasets (25–50%) achieved high-resolution representations of the main structures but failed to fully reconstruct certain elements, particularly the chimney. The point cloud was subsequently used to develop a HBIM-based model, enabling the reconstruction of structural components, including a hypothetical interpretation of the workers’ housing, currently in a near-ruined condition. These results demonstrate the effectiveness of UAV photogrammetry and HBIM as complementary tools for the documentation and reconstruction of industrial heritage, providing a solid basis for future restoration and adaptive reuse strategies.

Abstract: Predictive maintenance has become an essential component of smart manufacturing systems because it enables early detection of machine failures and reduces unexpected pro-duction downtime. However, conventional predictive maintenance approaches typically rely on centralized data collection and model training which may raise concerns regarding data privacy, communication overhead and data ownership in manufacturing environments. To address these challenges, this research proposes a privacy-preserving collaborative federated learning framework for predictive maintenance that can be deployed in distributed smart manufacturing systems. The proposed approach allows multiple factories to jointly train a machine failure prediction model without sharing raw data. In the framework, each factory trains a local multilayer perceptron (MLP) model using its own machine operational data, while a central server aggregates local model parameters using the Federated Averaging (FedAvg) algorithm to construct a global predictive model. The proposed framework was evaluated using the publicly available AI4I 2020 predictive maintenance dataset where multiple factories are simulated by partitioning the dataset into distributed clients. Experimental results show that the federated learning model achieves performance comparable to centralized machine learning baselines, reaching an accuracy of 99.93%, precision of 1.000, recall of 0.980 and F1-score of 0.990 while still preserving data privacy and IP protection across distributed participants.

Abstract: This study analyses the quality of electricity supply in the industrial sector of Lubumbashi (Democratic Republic of the Congo), highlighting disparities between different categories of industry. Despite a relatively high rate of access, the reliability and quality of service remain major constraints to industrial development. The analysis is based on a field survey of 160 industrial enterprises, representing approximately 71% of the identified population. A strati-fied random sample was used to represent the main categories (mining, agri-food, foundries, plastics and semi-industrial). The quality of the electricity supply was assessed using indicators such as the frequency and duration of outages, voltage drops, load factor and the use of gen-erators. Statistical analyses (ANOVA and regression) were used to compare performance and analyse the determinants of satisfaction. The results reveal significant variation. The mining sector has an aver-age load factor of 56.61%, indicating a relatively stable power supply, whilst the plastics and semi-industrial sectors exceed 100%, reflecting an overloaded grid. Power cuts are more frequent and longer in dura-tion in smaller units. Although 100% of industries are connected, the use of generators reaches 100% in certain categories. A significant neg-ative correlation (-0.58) is observed between voltage drop and satisfac-tion. These results confirm that service quality is inadequate and unevenly distributed, highlighting the need to strengthen infrastructure, improve voltage regulation and develop decentralised solutions.

Abstract: Front Running Simulation (FRS) is a Digital Twin–enabled capability that continuously replicates the current state of physical systems, predicts probable future states, and identifies actions to navigate to defined goals while minimizing the expenditure of scarce physical resources. Unlike traditional simulation, which operates offline with predefined initial conditions, FRS is continuously synchronized with reality through Digital Twin Instances (DTIs), allowing forward-looking simulation from the present state. FRS is based on three core activities—replication, prediction, and navigation—supported by data, Models of Reality (MoR), simulation, and information. Data enables replication. Simulation based on MoRs, enables prediction through causal and probabilistic methods; and information enables goal-directed action selection. The integration of Digital Twin Aggregates (DTAs) and Artificial Intelligence introduces Bayesian, data-driven prediction that complements physics-based simulation. This hybrid approach combines exploration of possible futures with rapid identification of probable outcomes. As the examples demonstrate, FRS shifts the focus from only adverse event avoidance to goal attainment under constraints, enabling proactive, information-driven decision-making. It provides a unifying Digital Twin FRS framework for Models of Reality, data, simulation, information, and AI to improve operational efficiency and effectiveness in complex systems.

Abstract: Accurate temperature prediction is essential for optimizing the microwave preheating of PET preforms prior to blow molding. A key challenge in this context is the strong dependence of electromagnetic field distributions and thermal responses on preform geometry, which varies substantially across product lines. Conventional neural network models trained on specific geometric configurations typically fail to generalize to unseen preform designs, requiring costly retraining for each new geometry. This work proposes a unified geometry-aware deep learning framework that predicts spatial temperature distributions across multiple preform designs using a single neural network model. The approach reformulates temperature prediction as a coordinate-level regression task conditioned on spatial location, geometric descriptors, process parameters, and structural region labels. A domain-bounded training strategy based on extreme feasible preform geometries is introduced, ensuring that predictions for intermediate designs remain within the interpolation regime of the network. The framework is evaluated on six distinct preform geometries, demonstrating that a single model can generalize reliably to new, unseen preform designs when their geometric parameters fall within the bounds of the training data. This is achieved through a domain-bounded training strategy that constructs datasets from the extreme feasible geometries, thereby converting the prediction of any intermediate design into an interpolation task. Since neural networks are inherently limited in their ability to extrapolate beyond the training domain, this formulation is essential for ensuring stable and accurate predictions across the full range of industrially relevant preform configurations. The proposed methodology provides a foundation for geometry-informed surrogate modeling in thermal process control and can be extended to other manufacturing systems characterized by strong geometric variability.