Engineering

Abstract: Steel weldability remains a central issue in materials engineering, affecting structural integrity, manufacturing reliability, and lifecycle performance across many industries. This paper examines weldability from an integrated perspective that combines metallurgical fundamentals, quality assurance methodologies, and emerging artificial intelligence (AI) tools. Key metallurgical aspects including phase transformations, heat-affected zone behavior, grain refinement, and microstructural heterogeneity—are discussed in relation to weld quality and mechanical performance. Limitations of conventional destructive and non-destructive inspection methods are considered in the context of increasingly complex welding requirements. The study further analyzes the role of AI-assisted approaches, such as machine learning, computer vision, and predictive modeling, in improving defect detection, process monitoring, and weld evaluation. By linking microstructural information with performance indicators and incorporating intelligent monitoring systems, the work outlines a data-driven framework for adaptive quality control and more reliable welding processes.

Abstract: This research develops a 12-week rolling-horizon mixed-integer programming model with an ECO-centric objective to address scheduling inconsistency caused by frequent engineering change orders (ECOs), incomplete bills of materials (BOMs), and production-line imbalance in server assembly. The model integrates BOM versions, workstation capacities, worker shifts, order priorities, and SLA constraints, while incorporating event-triggered incremental rescheduling, historical-solution warm starts, variable freezing, and adaptive neighborhood expansion. Eighteen months of production data from a server manufacturer in California, USA, were used for discrete-event simulation and validation. Results show that the proposed method reduces cumulative delay to 2,026 hours, overdue work orders to 121, peak work-in-process to 286 units, and schedule recovery time to 21.7 hours. The approach maintains schedule stability and production-capacity balance under multiple concurrent disturbances, providing a computationally feasible decision-support solution for dynamic scheduling in high-mix, low-volume server assembly environments, improving responsiveness to engineering changes, material shortages, equipment downtime, and rush orders.

Abstract: The permutation flow shop scheduling problem (PFSP) is a fundamental scheduling problem for which heuristic methods are widely used due to the vast size of its solution space. This study investigates whether solution populations generated entirely from random permutations contain structural information capable of guiding heuristic search. Independent random pools were generated for each problem instance, and pairwise precedence (P) and relative-position region (R) information was extracted from the Elite and Poor groups defined according to their objective function values. The Elite-only approach captures structural concentration among better-performing random solutions, whereas the Contrast approach captures frequency differences between Elite and Poor solutions. The extracted information was integrated into the NEH, NEH-TBKK2, KK2-style-NEH, FRB4-p1, and INEH-inspired methods while preserving Cmax as the primary decision criterion in all cases. In the constructive methods, the information is used only to distinguish among insertion alternatives tied in terms of Cmax, whereas in the reinsertion-based methods, it also guides the selection of search candidates. The proposed approach was evaluated on 140 benchmark instances, comprising 110 Taillard and 30 VFR instances. The results show that structural information extracted from random populations provides stronger and more consistent improvements, particularly in reinsertion-based methods, and that the algorithmic decision point at which the information is incorporated is as critical as the information content itself.

Abstract: The growing global demand for particleboards has generated large quantities of waste in recent years. This waste mainly arises from manufacturing processes, furniture industry offcuts, and end-of-life disposal. In response to waste management challenges, recycling and reuse technologies have advanced significantly. In this context, this study evaluates the feasibility of producing new particleboards using recycled particles obtained from laminated particleboard waste treated with oxalic acid hydrolysis. Three-layer panels were manufactured with varying proportions of recycled particles (0%, 25%, 50%, 75%, and 100%) in the core layer. Their average density, vertical density profile, physical and mechanical properties, as well as their formaldehyde emissions, were assessed. The results showed that the inclusion of recycled particles did not significantly impact the average density or the vertical density profiles of the panels. Thickness swelling and water absorption after 24 h immersion were similar across all panels, indicating good dimensional stability. Internal bond (IB) strength showed no consistent trend with increasing recycled particle content, and panels with 75% recycled particles achieved the highest IB strength (0.48 MPa). In contrast, bending properties decreased when the proportion of recycled particles exceeded 25%. All panels satisfied the requirements of the LD-2 Grade for low-density particleboards specified by the ANSI A208.1-2022 standard. Although formaldehyde emissions increased in panels with more than 25% recycled particles, they remained within acceptable limits up to 75% recycled material. These findings indicate that particleboards can incorporate up to 75% recycled particles in the core, while maintaining physical and mechanical properties comparable to those of the control panels.

Abstract: This research develops an Integrated Decision Framework for Reliability-Centered Maintenance and Renewable Energy Coordination (IDF-RCM-REC) to support sustainable industrial production under energy uncertainty. The study addresses the lack of unified models that jointly optimize maintenance policies and renewable energy dispatch, which currently leads to suboptimal reliability, cost, and sustainability outcomes. A multi-objective optimization framework integrating RCM-based reliability modeling, stochastic renewable energy coordination, and MCDM-guided solution selection is formulated and validated through a case study of an energy-intensive manufacturing facility. Results show a 16.6% reduction in total annual cost, 19.8% lower carbon emissions, 43.8% reduction in unplanned downtime, and a 26.1% increase in renewable energy utilization, alongside a shift toward predictive and condition-based maintenance strategies. The framework enables decision makers to evaluate trade-offs between cost, reliability, and sustainability and to select strategies aligned with organizational objectives. This work provides a practical, mathematically grounded tool for aligning maintenance and energy management in renewable-integrated industrial systems, advancing both theoretical understanding and operational practice in sustainable production.

Abstract: Medium-density fiberboard (MDF) is widely used for embossed door panels. The fiber refining process is crucial for determining both surface quality and mechanical performance of these panels. This study investigated how different refining conditions affect fiber granulometry, surface quality, and mechanical properties of embossed MDF door panels. Three key refining parameters were investigated: digester steam pressure (0.8 and 0.9 MPa), refiner specific energy (70 and 80 kWh/t), and refiner differential pressure (24, 35, and 50 kPa). Panel properties were evaluated in terms of vertical density profile, surface roughness, bending properties, and cleavage strength. Statistical analysis revealed that refining parameters significantly affected fiber granulometry, as well as the average and surface density of the panels. Surface roughness, however, showed minimal variation under the refining conditions considered. In contrast, the bending properties – modulus of rupture (MOR) and modulus of elasticity (MOE) – were significantly improved. Most treatments achieved MOR and MOE values that were similar to or higher than those of the control panels. Cleavage strength also varied with refining conditions but remained comparable to the control across all treatments. Overall, the results indicate that optimized refining conditions can enhance MDF door panel performance, particularly bending properties, without compromising surface quality or cleavage strength.

Abstract: Re-introducing recycled carbon fibre (rCF) into the manufacturing value chain signifi-cantly reduces environmental impacts. The fabrication of nonwoven mats made by card-ing rCF with thermoplastic filaments enable the production of preform suitable for manu-facturing and thermoforming structural parts and fusion bonding technologies. The in-duction welding process takes advantage of the random nature of such materials since the discontinuous fibres contribute to the formation of microcircuits heated by the eddy cur-rents related to the magnetic field enabling a susceptor-less welding process. In this paper, the optimal welding parameters of induction welding of rCF/maleic anhy-dride-grafted polypropylene (MAPP) laminates are been investigated. The compression molded laminates have been consolidated and experimentally characterized for compari-son with welded joints. The strength and fracture behaviour of the welded joints are gov-erned by several process parameters, including generator power, coil distance, translation speed, and compaction load. The optimal condition yielded an interlaminar shear strength of 11.7 ± 0.6 MPa, at least equal to that of the parent laminate. A Mode I initiation tough-ness of 560 ± 207 J/m² was measured, and all the joints failed cohesively. The actual welded area inside the joint is function of the welding process. The uniformity of the conductive fibre network was found as the key parameter on the extent of the weld. Although the weld was not uniform across the configurations, the findings support in-duction welding as an effective joining method for rCF nonwoven laminates.

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) are increasingly driving the digital transformation of manufacturing systems, enabling the transition from conventional process operation toward intelligent, adaptive, and data-centric production environments. This work presents the development of AI-enabled advisory systems for casting processes, integrating advanced data-driven models, reduced-order modeling techniques, and hybrid simulation frameworks to support real-time process prediction and optimization. The proposed approach leverages manufacturing data to establish predictive models capable of rapidly evaluating process conditions, optimizing operating parameters, and enhancing product quality while reducing material waste, energy consumption, and production costs. By combining physics-based understanding with AI-driven analytics, the framework facilitates real-time decision support, adaptive process control, and continuous performance improvement within modern manufacturing ecosystems. These capabilities contribute to the broader objectives of Industry 4.0 and emerging Industry 5.0 paradigms, including automation, connectivity, operational resilience, sustainability, and human-centered manufacturing. A representative industrial casting case study is presented to demonstrate the practical implementation of the framework, encompassing database generation, model training, validation, and deployment of predictive advisory tools for real-time manufacturing applications.

Abstract: Modern electronic assembly manufacturing relies on complex global supply chains, making it increasingly important to verify that every assembled component is authentic, expected, and consistent with the intended design. Existing automated inspection approaches typically formulate this problem as a classification task, providing limited insight into the physical evidence supporting their conclusions and often failing to distinguish expected manufacturing variation from genuine hardware integrity events. This paper presents a scenario-based method for hardware integrity verification that formulates component verification as an evidence-based reasoning process. Independent semantic observations and learned visual evidence are extracted from standard manufacturing images and evaluated against the expected observations associated with candidate manufacturing and hardware integrity scenarios, including normal production evolution, approved AVL substitutions, unexpected component changes, and counterfeit-related events. The method was developed using more than 6.5 billion component images collected from high-volume SMT manufacturing and enables transparent, explainable hardware integrity assessments. Representative examples demonstrate that the proposed methodology distinguishes expected manufacturing changes from hardware integrity violations using only standard production images. By automatically inspecting, identifying, verifying, and documenting every component assembled on every PCB, the proposed methodology establishes a practical foundation for component-level hardware assurance. The resulting digital record provides traceable forensic evidence for every assembled component, enabling scalable hardware integrity verification throughout the electronic assembly manufacturing process.

Abstract: This paper proposes a novel neuro-symbolic framework that augments Vision-Language-Action (VLA) models to assist high-precision operations in industrial assembly. While Large Language Models (LLMs) demonstrate expertise in interpreting natural language instructions, they often struggle with processing structured manufacturing data and lack long-horizon planning capabilities and deterministic physical awareness required for complex robotic execution. Furthermore, their susceptibility to generating physically unfeasible sequences and their lack of robust error-recovery mechanisms limit their autonomous deployment. To address these limitations, the proposed pipeline decomposes the assembly process into sequential stages of planning, technological grounding, adaptive execution, and recovery. Initially, the framework leverages LLMs to synthesize multimodal inputs—ranging from natural language directives to formalized product and process specifications—into a formal Hierarchical Task Network (HTN). To ensure physical feasibility, an external symbolic planner rigorously validates the HTN’s causal dependencies against established spatial and operational constraints. This verified logical sequence is subsequently compiled into a reactive Behavior Tree (BT) to guide the VLA model. During execution, a real-time object detection system and an atomic skill library dynamically translate the BT into grounded actions. Designed for human-robot collaborative environments, the architecture autonomously allocates standard operations to the robotic agent while safely deferring tasks requiring high dexterity to human operators. By combining the semantic adaptability of LLMs with the rigorous state validation of symbolic planners, this methodology ensures deterministic, safe, and efficient manufacturing workflows. The proposed architecture is evaluated against both physical and logical criteria using task-specific evaluation metrics.

Abstract: Polylactic acid (PLA) filaments are considered one of the most significant materials employed in the biomedical fields, particularly in bone fabrication. Hence, the development of bone fabrication is also deeply dependent on the tissue's pores and the resistance of the 3D-printed surface against the plastic deformation generated from indentation. To measure this indentation behavior for these types of fabricated parts, the D-Shore hardness (HS) is one of the most commonly used methods. Unfortunately, this method lacks a mathematical model computing the hardness number compared to the Brinell hardness (HB) method, since the measurement is implemented directly on the printed sample surface. Therefore, this study aims to establish a novel model computing the hardness number of the D-Shore depending on the experimental number of HB and HS. The parameters adopted in this work were (100, 150, and 200 μm) and (0˚/90˚ and -45˚/45˚) for the pore sizes (PS) and orientation layers (OL), respectively, using printed Micro-Samples from PLA filaments. However, the current work has utilized the Explicit Finite Element Analysis (EFEA) to predict the feasible design from these samples. Consequently, trial No. 4 at (100×100 μm2) and (-45˚/45˚) had an error ratio for the average of HB numbers, predictively, without exceeding 2% with experimental outcomes, as a feasible design. Additionally, the mathematical model proposed to compute D-Shore hardness has a correlation factor ranging from 99.55% to 99.99%, depending on experimental results for HB and HS, where the OL-Parameter in analysis of variance (ANOVA) was the dominant factor.

Abstract: Recent advances in Digital Twin technology focus on visualization, operator training, and real-time machine simulation, aligning with the Industry 4.0 paradigm. However, the transition toward Industry 5.0 demands human-centric approaches that integrate workers not merely as observers but as active, monitorable parts of the system. Despite growing research interest, mature end-to-end methodologies for creating and deploying reliable Human Digital Twins in industrial environments are still lacking. This paper introduces Industrial Meta-Human (IMHU), an end-to-end human-centered Digital Twin methodology designed to bridge this gap. By spanning the entire lifecycle, from human modeling to production deployment, IMHU leverages Unreal Engine simulation to generate accurate human models and synthetic data, allowing safe replication of hazardous scenarios without disrupting ongoing operations. The methodology integrates Artificial Intelligence to enable real-time monitoring and support data-driven decision-making. Deployed on a fully operational production line, IMHU includes a system integration layer based on a Service-Oriented Architecture, enabling seamless interoperability with legacy Industry 4.0 infrastructures. Experimental results demonstrate the feasibility and confirm the effectiveness of real-time human-state tracking, and underscore its potential to advance scalable, human-centered Digital Twin systems for Industry 5.0. The dataset will be available for research purposes upon acceptance. An overview of the proposed framework is available in the accompanying video demonstration: https://youtu.be/qPHSl0Wp7nY.

Abstract: We tackle the problem of job shop scheduling. Exact methods are limited to small instances, and traditional metaheuristics do not guarantee feasibility at each iteration. In this research, we propose a new two phases hybrid metaheuristic. The first phase uses Gradient Descent on a convex energy function to quickly construct a feasible solution by fixing the operations sequence. We provide a mathematical proof of convergence for this phase to a feasible solution. The second phase applies an Iterated Local Search to explore solutions space and minimize the makespan.This separation of objectives guarantees convergence of initial phase and simplifies parameter tuning. Experiments on standard FT and LA benchmarks show that EGD-ILS achieves competitive results with reduced computation time.

Abstract: The JSSP is recognized as one of the most difficult combinatorial optimization problems because it as well and indeed can be classified as an NP-hard problem. This paper introduces two metaheuristic frameworks which both make use of Iterated Local Search, but differ fundamentally from each other in the way they represent solutions. The initial metaheuristic, called the Priority-based metaheuristic, creates schedules using priority dispatching rules guided by ILS. In contrast, the second metaheuristic, referred to as the Permutational coding-based metaheuristic, employs a permutation coding approach for every operation and directly implements neighborhood moves on this sequence. Both metaheuristics aim to reduce makespan. To evaluate their performance, computational experiments were performed in MATLAB on recognized benchmark datasets to analyze the quality of solutions. The main goal is to identify which representation provides a better equilibrium between exploration and exploitation abilities when combined with the same ILS methods. The JSSP is known to be one of the complex problems of combinatorial nature due to the fact that it belongs to the class of NP-hard problems. In this study, two novel methods of solving the JSSP are presented and evaluated. The two methods are that both make use of the Iterated Local Search (ILS) algorithm. The first method, termed Priority-based metaheuristic, is the one which constructs schedules according to priority scheduling rules based on ILS. On the other hand, the second method, which is termed Permutational coding-based metaheuristics, is built on the idea of coding each operation in terms of a permutation of the operation and putting the neighborhood operations directly on the short string. The two metaheuristic methods are aimed at minimizing the makespan. In order to test the performance of the two methods, computational tests are conducted in MATLAB using the standard benchmark tests to investigate the performance of the problems solved.

Abstract:

Due to the superior thermal, mechanical and chemical properties of ceramics, metallized ceramics are widely used as circuit carriers and interconnect devices wherever standard polymer-based circuit boards come to their limits. 2D metallization represents the current state-of-the-art. The metallization of 3D shaped ceramics cannot be achieved with standard metallization techniques and is still a field of research. In this study laser induced direct metallization (LDM) is applied, where a pulsed laser is used to locally activate the surface of a Cr₂O₃ doped ZTA ceramic. Subsequently a selective electroless copper plating on the laser irradiated areas is performed. Laser power, pulse repetition frequency, pulse overlap and the amount of surpasses were varied systematically in order to determine the parametric sensitivity of the ablation and metallization behavior for pulsed infrared laser structuring. It was found that ablation is necessary for the metallization and the peak fluence is the governing factor for the ablation and metallization process. It was further shown, that the ablation can be well predicted with an accumulated fluence which includes the pulse overlap. 3D ceramic substrates were successfully metallized applying an optimized set of laser parameters.

Abstract: Task package partitioning is a key step in shipbuilding. Efficient and balanced task package partitioning is a top priority for modern shipyards. The partitioning scheme is one of the crucial issues in shipbuilding. However, the multidimensional features of intermediate products—the intrinsic basis on which task units should be grouped into packages—are often overlooked in existing methods, resulting in low intra-package cohesion, high inter-package coupling, and imbalanced load distribution. To address this problem, a multi-objective optimization model is established to optimize task package partitioning. First, a three-objective model is constructed to maximize cohesion, minimize coupling, and balance the workload. Second, phase-homogeneity hard constraints and man-hour bounds are introduced to ensure engineering feasibility. Finally, a hybrid Genetic Algorithm - Branch and Bound solution framework is designed, in which global search is performed by GA and local refinement is handled by B&B. A practical case of an Hull Construction Project of 11000 DWT Bulk Carrier is used to evaluate the performance of the proposed method. Satisfactory results are achieved with effective convergence, as cohesion is improved, coupling is reduced, and workload distribution is balanced, which provides robust support for engineering decision-making.

Abstract: Additively manufactured components require machining of functional surfaces to meet geometric requirements. Due to low stiffness and non-nominal as-built geometry, they are susceptible to milling-induced shape deviations. This paper presents a hybrid process simulation for predicting shape errors in compliant metallic laser powder bed fusion components. The method combines real-geometry-based technological NC simulation, quasi-static force prediction, finite element-based structural response simulation, and surface reconstruction between roughing and finishing to enable multistage operation. The approach is validated for linear and non-linear toolpaths with varying immersion angles and compliance conditions. The results show reproduced force profiles, while magnitude deviations highlight the relevance of deformation-dependent engagement feedback in high-compliance regions. An analytical back-calculation based on the effective engagement cross-section reveals that accounting for deflection-induced engagement reduction reduces force deviations. During roughing, maximum shape errors for linear and non-linear toolpaths are overestimated by 4–5 %, and critical high-error regions are identified. The reconstructed intermediate geometry after roughing is essential for finishing, since neglecting geometry feedback underestimates finishing forces. With geometry feedback, the maximum finishing shape error is predicted as 0.090 mm, while the measured value is 0.086 mm. The simulation captures dominant quasi-static shape-error regimes and supports process-chain-oriented prediction in additive-subtractive manufacturing.

Abstract: Chocolate-handling process currently relies on manual labor: operators open cardboard boxes, cut packaging, unwrap each chocolate bar by hand, and load the unwrapped product into a Baskett tempering machine. This manual dependency introduces process variability, operator fatigue, and a throughput ceiling imposed by the Baskett’s fixed 70-minute batch cycle. This paper presents the design and Digital Twin simulation of a semi-automated workstation that integrates a feeder, a collaborative robotic arm with a custom friction-roller gripper, a waste-handling subsystem, and an Industry 4.0 monitoring stack (SCADA, HMI, and an AI-based decision-support layer) to remove the manual unwrapping bottleneck. The material flow was modeled in Siemens Plant Simulation, and the robotic operation was validated in Process Simulate, while a Node-RED/Firebase/React architecture emulated the supervisory control layer. Over a simulated 24-hour period, the workstation processed 18,432 individual chocolate bars across 12 production batches while sustaining synchronization with the Baskett cycle. The Baskett remained the limiting resource, occupied 76.09% of the time, while the robotic subsystem operated well under capacity (conveyor utilization 100%, gripper utilization 42.67%, pick-and-place utilization 38.4%), indicating margin for future throughput increases. The results confirm that the proposed cell eliminates the manual unwrapping task while remaining compatible with existing downstream equipment, and they identify the Baskett cycle time as the priority target for further improvement.

Abstract: This paper presents a semester-long engineering project that develops a multi-layer Digital Twin for the chocolate-bar preparation stage, a bakery producer in Monterrey, Mexico. In the current process, operators manually open shipping boxes, separate multi-bar packages, peel individual wrappers, discard packaging waste, and transport exposed bars to a downstream decoration station with labor-intensive activity with no real-time visibility into throughput, cycle time, or quality events. We propose a compact, inline semi-automated preparation cell a loading hopper, vibratory bowl feeder, food-grade conveyor, pneumatically actuated guillotine cutter, counter-rotating roller extractors, and separated collection paths for product and waste and validate it across five complementary technology layers: (1) discrete-event simulation in Siemens Tecnomatix Plant Simulation; (2) 3D spatial and kinematic validation in Siemens Process Simulate with CAD solid models; (3) a Node-RED operator dashboard; (4) a Google Firebase Realtime Database as the shared live data backbone; and (5) Google Gemini for alarm grouping, predictive maintenance, setpoint optimization, and SOP-style decision support. The simulation sustains a conveyor output of 792 units over the modeled horizon, with extraction-station utilization of 88.83% and 82.00% and blocking rates approaching 89.78–89.26%, indicating that buffer and release-logic tuning is required before physical deployment. CAD-based validation confirms the station's spatial and kinematic feasibility, and the SCADA/HMI/AI layer demonstrates functioning live monitoring and AI-generated operational guidance. A preliminary economic analysis estimates an initial investment near USD 19,000, an annual labor saving near USD 8,900, and a payback period of approximately 2.1 years. The results indicate that discrete-event simulation, CAD validation, and SCADA 4.0/AI integration can jointly de-risk a semi-automated retrofit before capital is committed to fabrication.

Abstract: Manual unwrapping of chocolate bars in a family-owned confectionery in Monterrey, Mexico, is a labor-intensive process that produces inconsistent cycle times, high product damage, and elevated payroll costs. This paper presents the design, discrete-event and kinematic simulation, and financial validation of a collaborative robotic cell that automates the gripping, unwrapping, and placement of chocolate bars using a Digital Twin methodology. A macroscopic process model was built in Tecnomatix Plant Simulation to quantify throughput, utilization, and bottlenecks, while a microscopic three-dimensional model was built in Tecnomatix Process Simulate to validate robot reachability, collision-free trajectories, and kinematics for a 6-degree-of-freedom UFACTORY xArm 6 cobot fitted with a custom compliant gripper. A Siemens/Schneider PLC layer, a Node-RED edge gateway, a Firebase database, and a large-language-model-based analytics loop were integrated to provide SCADA 4.0 supervision and closed-loop setpoint correction. Simulation results show that the automated cell increases throughput from 356 to 576 successfully unwrapped units per 30 minutes (a 61.8% improvement) while sustaining a 96% quality rate and eliminates the 100% input-side blockage observed in the manual process. The financial analysis indicates a total capital investment of approximately $425,766 MXN, an annual net benefit of $179,890 MXN, a projected Internal Rate of Return of 31.2%, and a payback period of 2.3 years. These results indicate that a compact, food-grade collaborative robotic cell is a technically feasible and financially attractive solution for small and medium-sized confectionery producers seeking to automate deformable-product handling.