Search Results (1,553)

Production data transparency in smart manufacturing simultaneously enhances and impairs employee performance across organizational contexts. Existing research has not resolved this theoretical contradiction. Drawing on socio-technical systems theory, cognitive appraisal theory, and conservation of resources theory, this study develops a dual-pathway model. Data transparency influences adaptive performance through a bright empowerment pathway and a dark surveillance pathway mediated by EPM-induced strain. Procedural justice of data governance and digital self-efficacy operate as a perceived-institutional boundary condition and an individual-capability boundary condition, respectively. Latent moderated structural equations were applied to survey data from 412 employees in Chinese smart manufacturing enterprises. Results support both pathways and reveal a theoretically consequential asymmetry between these boundary conditions. Johnson–Neyman analysis indicates that institutional justice attenuates the strain pathway to non-significance within the observed distribution of responses. Conversely, digital self-efficacy requires near-ceiling levels to achieve the same pattern. This asymmetry indicates that continuous moderation produces sharply different practical outcomes across the observed data range. Institutional and individual remedies therefore address the visibility paradox on different practical scales. Governance adequacy represents a more attainable managerial lever than individual capability development. These findings advance socio-technical systems theory by detailing the asymmetric buffering capacities of different organizational resources. Full article

In this paper, we investigate a simulation-based optimization framework that implements discrete-event simulation with evolutionary search methods to optimize stochastic manufacturing systems efficiently. The proposed methodology couples a Tecnomatix Plant Simulation model with a MATLAB R2025b-based optimization environment using a data exchange interface, allowing for the iterative assessment of complex manufacturing systems. The study examines an adaptive replication strategy designed to manage stochastic variability in simulation outcomes. In the proposed method, the required number of simulation runs are determined dynamically based on confidence interval estimation. The stopping criterion is specified using a 95% confidence interval, ensuring adequate statistical accuracy while decreasing excess computational effort. The framework allows multiple performance indicators, such as throughput, congestion levels, and machine failures, which are built into an objective function. The optimization is driven by a (1, λ)-evolution strategy with Gaussian mutation and adaptive step-size control, allowing robust search in noisy objective function. However, thanks to the framework presented, it is also possible to apply other optimization algorithms. A case study of a manufacturing system was built and modeled in Tecnomatix Plant Simulation to validate the proposed methodology. In comparison with the baseline production configuration in one of the simulation runs, the suggested framework reduced the objective function by 43.36%. Benchmark experiments demonstrated that the adaptive replication strategy achieved a solution quality comparable to fixed replication schemes while requiring fewer simulation evaluations on average, thereby reducing the computational effort without compromising statistical reliability. The benchmark comparison showed that the adaptive replication strategy improved the objective value by up to 17.20% compared with fixed-replication strategies while requiring substantially less computational time than the fixed-20 and fixed-30 strategies. The robustness analysis further demonstrates that the adaptive replication strategy produces consistent optimization results across independent runs despite the stochastic nature of both the simulation model and the optimization process. From an industrial perspective, the proposed framework provides a practical decision-support tool for the optimization of manufacturing systems under uncertainty, enabling more reliable parameter tuning with reduced computational effort and facilitating the implementation of digital twin technologies. Full article

Digital-twin-enabled human–machine collaboration (HMC) has increasingly been proposed as a system-level approach for connecting human operators, robots, sensors, artificial intelligence modules, and manufacturing resources. However, the literature varies substantially in what is called a digital twin, how physical and virtual models are coupled, whether models are updated from physical data, and how far systems have progressed beyond simulation or controlled laboratory demonstrations. This structured integrative review examines the conditions under which a digital twin can function as an integration layer for HMC in sustainable smart manufacturing, rather than assuming that such integration is already established industrial practice. The literature corpus was assembled through searches of the Web of Science Core Collection, Scopus, and IEEE Xplore, complemented by Google Scholar-based citation tracking and backward and forward citation tracing. The core search focused on studies published from 1 January 2020 to 5 August 2026, while earlier seminal studies were retained to support definitions and historical context. Studies were screened using explicit criteria for manufacturing relevance, physical–virtual coupling, state synchronization or model updating, feedback capability, and validation setting, and were critically coded by model type, integration mechanism, deployment maturity, and sustainability evidence. The review compares multimodal perception and human-state modeling, intention understanding and augmented interaction, task allocation and shared planning, digital-twin architectures, adaptive control and safety verification, and human–AI decision-making. The evidence indicates that digital twins are promising as coordination and verification layers, but many reported systems remain conceptual, simulation-based, or limited to controlled physical prototypes. Key barriers include model fidelity, online model updating, real-time synchronization, cross-platform interoperability, safety assurance, human-data governance, and the limited availability of directly measured sustainability outcomes. Future work should prioritize validated hybrid models, traceable model-update mechanisms, staged virtual-to-physical deployment, interoperable data contracts, and longitudinal evaluation of technical, human, economic, and environmental performance. Full article

Digital identity and battery-passport systems can make electric-vehicle battery lifecycle records traceable, but traceability alone does not guarantee verifiable evidence or physical recycling compliance. We develop a normative two-stage Stackelberg model in which a regulator chooses audit intensity and verified incentives and a representative obligated firm jointly chooses pre-verification evidence quality and recycling compliance. The benchmark is stress-tested under reduced eligibility-confirmation effectiveness, alternative evidence–recycling interaction, decentralized manufacturer–recycler decisions, and implementation costs. Baseline traceability can leave evidence quality at the reporting floor and compliance incomplete, while additional responsibility exposure can conditionally increase recycling compliance while reducing evidence quality. Coordinated verified incentives improve both decisions and welfare in the benchmark complementarity domain. At zero eligibility-confirmation effectiveness, the fixed positive-rate package can become marginally welfare-inferior under finite capacity; adverse interaction can reverse a cross-effect, decentralization creates coordination underinvestment, and high payment or startup costs create single- or no-positive-payment regions. The model is therefore a forward-looking governance analysis rather than an empirical evaluation or legal calibration of current Chinese or European Union rules. Full article

Prototyping has evolved from a simple representational artifact into a central mechanism for learning, communication, risk reduction, and decision-making in engineering design. Despite its widespread adoption across engineering disciplines, existing research remains fragmented across domains, methodologies, and application contexts, making it difficult to distinguish broadly applicable principles from context-specific practices. This paper presents a critical review of prototyping research in engineering design, synthesizing evidence from peer-reviewed journal articles and conference papers from foundational studies of the 1980s to recent developments in rapid prototyping, additive manufacturing, digital engineering, and Industry 4.0 systems. A thematic literature review was conducted to identify recurring principles, domain-dependent variations, emerging trends, and persistent limitations in current prototyping practices. The review examines key factors influencing prototyping effectiveness, including purpose, fidelity, timing, stakeholder involvement, modeling and analysis, risk management, economic considerations, and learning-oriented iteration. Particular attention is given to how uncertainty influences prototyping decisions and the ways in which different uncertainty conditions influence the selection, scope, and implementation of prototyping activities. The findings indicate that prototyping is best understood as a context-dependent decision-support activity whose effectiveness depends on the uncertainties, constraints, stakeholders, and design objectives associated with a specific engineering problem. Although several common principles emerge across engineering domains, substantial differences exist in how prototypes are used to support design decisions and system validation. The review identifies research gaps related to uncertainty-driven fidelity selection, integration of modeling, experimentation, and verification activities, and the limited availability of systematic guidance for selecting prototyping strategies across diverse engineering contexts. Future research should focus on generalized prototyping frameworks, quantitative decision-support methods for uncertainty management, enhanced stakeholder integration, and the continued convergence of physical and virtual prototyping environments in next-generation engineering systems. Full article

Battery packs for new energy vehicles have evolved from simple load-bearing and protective assemblies into multifunctional safety structures integrating structural support, crash protection, thermal-runaway mitigation, flame retardancy, electrical insulation, electromagnetic interference shielding, waterproof sealing, and long-term reliability. Fiber-reinforced polymer composites are promising for upper covers, underbody shields, trays, cross beams, side frames, and local protective structures because of their low density, corrosion resistance, design flexibility, and functional-integration potential. However, composite-part performance is strongly governed by forming. Resin flow, impregnation, curing or cooling shrinkage, fiber orientation, filler dispersion, and interfacial bonding may induce voids, dry spots, resin-rich regions, delamination, warpage, and fiber waviness, thereby affecting load bearing, sealing, thermal protection, and durability. This review focuses on composite-forming technologies for new energy-vehicle battery packs. It summarizes component-level service requirements and material systems and compares representative forming routes, including sheet molding compound (SMC), prepreg compression molding/wet compression molding (PCM/WCM), resin transfer molding/high-pressure resin transfer molding (RTM/HP-RTM), vacuum-assisted resin transfer molding (VARTM), long-fiber thermoplastic direct processing (LFT-D), glass-mat thermoplastic (GMT), thermoplastic sheet forming, pultrusion, and multi-material joining. These routes are evaluated from six dimensions: material form, forming cycle, typical defects, representative mechanical performance, applicable components, and engineering maturity. The review further discusses defect mechanisms, performance effects, detection and control methods, and the roles of in-line monitoring, non-destructive testing, process simulation, machine learning, and digital twins in closed-loop quality manufacturing. Finally, engineering challenges are examined in multi-material joining, thermal-safety integration, low-carbon recycling, and standard certification. Composite-material battery-pack structures should therefore be developed as coordinated design and closed-loop manufacturing systems linking materials, processes, defects, performance, and validation. Full article

Background/Objectives: To evaluate the feasibility and the learning curve of an insourced, clinician-performed three-dimensional (3D) image reconstruction workflow using Synapse3D™ software in a robotic urologic surgery setting. Methods: In this prospective single-centre case series, 20 consecutive patients scheduled for robotic kidney surgery were enrolled over a 30-day period. Three urology residents without prior experience in 3D reconstruction completed a structured two-day training programme including eight supervised practice reconstructions. Subsequently, each one reconstructed all 20 study cases independently. Reconstruction time was recorded for every reconstruction. After surgery, the two operating surgeons independently rated each model on a 5-point Likert scale for anatomical fidelity, defined as the correspondence between the model and the anatomy encountered intraoperatively. Results: All 60 reconstructions were completed successfully, with no failed reconstructions, no DICOM (Digital Imaging and Communications in Medicine) import errors and no software crashes; assistance from the manufacturer was never required. Mean reconstruction time was 19 min (range 11–29 min). Reconstruction time decreased by 0.63 min per case (95% CI 0.51–0.74; p < 0.001). Median time fell from 23 min in the first ten cases to 15 min in the last ten (p < 0.001), and the reduction was significant for each operator analysed separately. Anatomical fidelity was rated 4 (IQR 1) by both surgeons. The two surgeons rated overall system usability 3/5 and 5/5. Conclusions: Insourced 3D reconstruction with Synapse3D™ is technically feasible and is learned rapidly and reproducibly by urology residents after limited training. Full article

Digital empowerment has become deeply embedded in global production networks, yet its implications for sectoral energy systems remain insufficiently understood. This study investigates whether digital empowerment is associated with sectoral energy-structure transition and through which channels. Using matched World Input–Output Database accounts for 2000–2014 and applying the Hypothetical Extraction Method (HEM), we measure digital empowerment at the country–sector level and examine its association with sectoral energy-use outcomes. The estimates indicate that digital empowerment is associated with a lower fossil-fuel share and lower total sectoral energy consumption in the full sample, although the associations differ across non-fossil energy categories, sectors, and development stages. Additional channel-based tests show that digital empowerment is associated with lower energy intensity, improved global value-chain positioning, and higher export technological sophistication. Both domestic and foreign digital inputs contribute to this relationship, and manufacturing sectors show a clearer association with lower total energy consumption than service sectors. Developing economies exhibit weaker compositional adjustment than developed economies. These findings provide empirical evidence for integrating digitalization into energy transition strategies and offer practical pathways for aligning digital economic development with sustainable energy objectives. Full article

Advancements in pulmonary drug delivery have enabled effective treatment approaches for more severe disease conditions, such as chronic obstructive pulmonary diseases, asthma, cystic fibrosis, and other pulmonary disorders, via targeted, sustained, and immediate drug delivery routes with minimal systemic side effects. However, conventional delivery systems have many limitations, such as poor drug targeting, adherence, and deposition, which ultimately cause variations in drug profiles and therapeutic efficacy. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled the development of smart inhaler drug–device combination systems for personalized therapy using predictive formulation parameters, design variables, device performance, and inhalation pattern monitoring. Advanced AI techniques, such as artificial neural networks, deep learning, random forests, support vector machines, deep learning algorithms, and computational modeling, predict the mass median aerodynamic diameter (MMAD), fine-particle fraction (FPF), emitted dose, and regional lung deposition. Smart inhalation devices coupled with digital sensors and computing systems enable the real-time monitoring of inhalation profiles and adherence. Moreover, AI- and ML-enabled Quality by Design (QbD) and digital twin framework technologies enhance the optimization of manufacturing process parameters, consistency, robustness, and scale-up performance. Although several developments have been reported, there is still room for improvement in terms of data heterogeneity, algorithm transparency, interpretability, cybersecurity, regulations, and long-term clinical standardization. This review emphasizes the use of AI to improve the performance of pulmonary drug delivery through smart inhaler drug–device combination therapies, focusing on technological advancements, formulation optimizations, smart inhalers, regulatory issues, current limitations, and future perspectives of AI-based pulmonary drug delivery. Full article

The increasing complexity of cyber–physical manufacturing systems demands simulation architectures that scale with the physical plant without a proportional growth in engineering effort. This paper introduces a formal, symmetry-based framework for behavioral reuse in arbitrary cyber–physical manufacturing systems, implemented within the Siemens NX Mechatronic Concept Designer (MCD), version NX 2506, environment. Symmetry is treated rigorously, as an equivalence relation induced by a symmetry-group action over the set of plant components, and three exploitable classes are defined on this basis: structural symmetry, arising from replicated kinematic configurations; functional symmetry, arising from shared behavioral specifications across instances of a common component class; and temporal symmetry, arising from synchronized cyclic behavior across concurrent actors. From these definitions, a four-condition behavioral inheritance protocol is derived, specifying the prerequisites under which a single behavioral library template is correctly instantiated across an arbitrary number of interchangeable components. The framework is demonstrated on a production cell comprising ten conveyor sections, nine CNC machining centers (5-axis, X/Y/Z/A/B/SP), and two COMAU NJ420-3.0 manipulators—each a 6-axis articulated arm extended by an external linear rail to seven controlled axes—governed through Siemens Sinumerik RunMyRobot/Direct Control and exercised in a co-simulation environment that integrates a Create MyVirtual Machine (CMVM) Software-in-the-Loop (SiL) controller, a Simit Model-in-the-Loop (MiL) communication layer, and MCD for kinematic and behavioral emulation. Using the number of independent behavioral configuration operations as the effort metric, the symmetry-driven approach reduces machining-center configuration effort by 88.9% (from nine independent configurations to one template instantiated nine times) and robot behavioral configuration effort by 100% (both manipulators inherit from a single seven-axis library entry), while preserving full kinematic and signal-level fidelity. The inheritance mechanism is shown to tolerate heterogeneous kinematic substitution: a COMAU NJ420-3.0 may be replaced by any kinematically equivalent 6-axis manipulator in the RunMyRobot database without behavioral reconfiguration. The component and capability mapping matrix (CCMM) introduced in prior work is extended with a symmetry-annotation layer that explicitly encodes instance relationships and inheritance chains, providing a structured input to automated behavioral-deployment workflows. The results establish symmetry-based modular simulation as a principled and scalable methodology for industrial digital-twin development in multi-robot manufacturing environments. Full article

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 singular value decomposition (SVD)-based reduced-order models with a Variational Autoencoder with Arbitrary Conditioning (AC-VAE) 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 Horizontal Direct Chill (HDC) continuous 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. Full article

Energy-intensive foundries require methods to explore trade-offs among delivery performance, production horizon, electricity use, and cost when industrial data are incomplete. This paper presents an offline, process-level digital twin case study for the melting and casting area of a Danish cast-iron foundry. A multi-agent simulation represents production orders, enterprise resource planning and manufacturing execution system functions, induction furnaces, holding furnaces, crane-based transfer of molten metal, vertical moulding lines, the operating calendar, and electricity cost accounting for the induction furnaces. The model is assessed through boundary definition, assumption registration, implementation checks, material flow plausibility, a diagnostic comparison of furnace temperature, controlled scenario experiments, and local sensitivity analysis. These activities support internal consistency and bounded interpretation but do not constitute independent operational validation of the full production system. In the simulated 200-order monthly case, First-Come-First-Served and Earliest Deadline First complete the same 288,620 pieces and 5482.00 t. Earliest Deadline First increases the simulated on-time completion rate from 87.5% to 100%, while makespan, model-estimated electricity use by induction furnaces, and model-estimated electricity cost increase by 7.52%, 0.58%, and 3.42%, respectively. The case indicates that deadline-oriented sequencing may improve delivery performance but lead to a longer production horizon and higher energy use and cost within the defined model boundary. The contribution is an auditable foundry-specific modelling workflow that links heterogeneous data conditions to modelling choices, supporting evidence, and interpretation limits. The model is therefore intended for preliminary offline scenario exploration rather than validated operational decision support. Full article

Pseudocereals such as quinoa, amaranth, and buckwheat have attracted increasing attention as ingredients for extruded snack products because of their nutritional value, gluten-free status, and content of bioactive compounds. The quality of extruded products is governed by complex interactions among processing variables, including barrel temperature, screw speed, feed moisture content, and formulation characteristics. As a result, mathematical modelling has become an important tool for predicting product properties and identifying suitable processing conditions. This review summarizes modelling approaches applied to extruded food products with a focus on pseudocereal extrusion. Particular emphasis is placed on response surface methodology (RSM), artificial neural networks (ANNs), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), and hybrid optimisation strategies. Published studies indicate that RSM remains the most commonly used approach because of its simplicity and interpretability, while ANN-based models generally provide much higher predictive accuracy when strong nonlinear relationships are present. The widespread use of small experimental datasets and limited external validation remains a major challenge for the practical implementation of advanced machine-learning models. This review examines the strengths and limitations of current modelling approaches and discusses future opportunities for integrating predictive models with digital manufacturing frameworks. Full article

Mesenchymal stem cells (MSCs) are a critical biological resource for regenerative medicine, immunomodulation, and personalized cell therapy. Three structural problems persist: (1) the absence of standardized, quantitative quality indicators; (2) insufficient tamper-proof traceability throughout the manufacturing and banking lifecycle; and (3) the lack of a personalized matching system linking MSC batch characteristics to patient-specific clinical requirements. This paper proposes the MSC Digital Assetization Framework (MDAF), an applied engineering framework that addresses all three problems at the architectural and prototype level. Here, digital assetization—the transformation of a biological product into a structured, traceable, and transferable digital quality record within a multi-institutional trust infrastructure—denotes verifiable, traceable, quality-certified digital recordization of MSC batches, not tokenization or financial trading. The quality engine integrates morphological, FLIM-derived metabolic–proliferative, donor blood panel, flow cytometry, and manufacturing metadata inputs through a bidirectional Cross-Attention fusion module, yielding a continuous MSC quality score (MQS, 0–100) and an S/A/B/C/D five-tier grade. Privacy-preserving verification is implemented via two independent Groth16 zero-knowledge proof circuits: a Release Eligibility Proof (REP, MQS ≥ 70) and a Premium Quality Proof (PQP, MQS ≥ 85). A Hyperledger Besu QBFT permissioned blockchain with smart contracts provides immutable lifecycle traceability and DID-based access control. In a synthetic data pilot (n = 2000), the system demonstrated engineering feasibility across all five subsystems. These results are engineering pipeline feasibility benchmarks on synthetic data; biological and clinical validation using real MSC data is mandatory follow-on research. Full article

This paper presents the design and development of a laboratory-scale 3D printing platform intended for research on sustainable construction materials. The growing interest in low-carbon and locally available materials, including clay, geopolymers, recycled aggregates, and bio-based composites, has increased the need for flexible experimental printing systems. However, most existing construction 3D printers are designed for industrial applications and remain costly, bulky, or limited to specific material categories. The proposed platform was developed using a Model-Based Systems Engineering approach in order to structure the design process and establish links between user needs, system requirements, functions, and physical components. The platform is based on modular Cartesian architecture and includes interchangeable extrusion systems. A syringe extruder is used for relatively fluid materials such as clay slurries, ceramic pastes, gypsum-based mixtures, and fluid geopolymers, while a screw extruder is designed for more viscous materials such as mortars, cement-based mixtures, and dense geopolymer pastes. The system also integrates motion-control components, material feeding devices, monitoring elements, and safety functions to ensure stable and repeatable printing conditions. The platform is intended to support the evaluation of printability, material flow, layer deposition, dimensional stability, and interlayer bonding. By combining a flexible hardware configuration with an MBSE-based design methodology, the proposed system provides a practical research tool for the development and validation of sustainable construction materials. It also creates opportunities for future work on multi-material printing, automated process control, and digital manufacturing applications. Full article