Search Results (1,114)

Wire-arc coatings have received substantial attention for corrosion protection; however, poor bonding often leads to delamination, corrosion initiation, and costly re-coating of structural components. This review combines bibliometric mapping with a focused technical synthesis to clarify how bonding performance has been studied in wire-arc coatings. Specifically, publication trends, keyword co-occurrence networks, and country-level co-authorship maps are used to map the evolution of the field and position adhesion-related studies within the broader literature. The analysis of 762 wire-arc coating publications from Web of Science (among 13,314 thermal spray coating records) reveals that research is centered on microstructure, mechanical properties, and corrosion resistance, with growing links to wire-based additive manufacturing. Keyword co-occurrence networks demonstrate clear process–structure–property relationships, while country-level collaboration maps highlight the leadership of China, the USA, and Germany. Critical to note, only eight publications systematically investigate the combined effects of substrate roughness, coating thickness, and Zn-Al coating composition on bond strength—representing less than 0.01% of the thermal spray literature. This pronounced research gap underscores the novelty of the present review, which synthesizes existing knowledge on adhesion mechanisms, identifies key process parameters, and establishes a research agenda to optimize wire-arc coatings for infrastructure corrosion protection. The technical synthesis highlights that adhesion is governed by the coupled effects of surface preparation (roughness and topography), coating build-up (thickness), and spray conditions (e.g., standoff distance and substrate preheating), which together influence coating microstructure and failure modes. These findings provide a structured framework to guide parameter selection for durable coatings. Full article

Digitalization has reshaped dental laboratory processes through digital tools and artefacts supporting clinician–laboratory collaboration; however, repeated iterations still increase coordination effort and extend delivery times. This study examined how the custom abutment process was executed in a dental laboratory and identified where and why iterations occurred during computer-aided design (CAD) modelling, design verification, and manufacturing preparation. Ten completed orders were selected, and their event log information was analyzed using process mining in Disco, complemented by contextual inquiry with domain practitioners. The analysis reconstructed observed execution from order initiation to delivery and derived a reference representation summarizing the most frequently observed ordering of actions. Across the ten orders analyzed, nine exhibited at least one iteration. Iterations were most frequently observed as returns between CAD modelling and design verification and occurred in four orders, while rescanning occurred in two orders due to insufficient or incompatible initial scan information. Contextual inquiry linked repeated action sequences to changes in digital artefacts and communication exchanges, indicating that iterations were associated with incomplete information or differences in interpretation across roles. The findings show that combining process mining with contextual inquiry enables the identification of iterations and clarifies the conditions under which they emerge. Full article

The rapid evolution of live-streaming commerce has reshaped retail supply chains, shifting market dominance from manufacturers to influential streamers. Despite this shift, the internal mechanisms of selling efforts and paid traffic acquisition remain underexplored. To bridge this theoretical gap, we develop a game-theoretic framework to model the endogenous power structure and compare the streamer-led top-tier (KS) mode and the brand-led ordinary (MS) mode. Our analytical results reveal three key theoretical insights. First, we establish strict positive monotonicity between streamer influence and equilibrium decisions. Regardless of the power structure, an increase in influence consistently drives the streamer to intensify operational inputs while simultaneously inducing the brand to raise the direct selling price. Second, consumer sensitivity acts as a positive driver of the top-tier mode. Higher sensitivity motivates the streamer to scale up sales efforts and paid-traffic volume, which corresponds to an optimal increase in the brand’s retail price. Moreover, the top-tier mode exhibits negative sensitivity to operational costs. We prove that rising costs lead to a significant reduction in the streamer’s operational portfolio and, consequently, to a decrease in the brand’s price, indicating that the high-input equilibrium is constrained by cost frictions. From a managerial perspective, numerical experiments reveal not a “Consensus on Scale” but a “Conflict on Structure.” Specifically, brands maximize profit by collaborating with top-tier streamers, while streamers maximize profit by attaining top-tier influence. However, the brand receives more profit by relinquishing channel leadership with respect to the decision hierarchy. In contrast, the streamer is less profitable as a leader than as a follower due to the “leadership trap,” in which greater operational burdens outweigh first-mover advantages. Full article

With the transition from Industry 4.0 to Industry 5.0, human–robot collaborative assembly (HRCA) has progressed from physical copresence to cognitive integration and knowledge sharing. Digital twins (DTs) serve as enabling technologies that connect physical and virtual spaces. Support is provided for dynamic, safe, and human-centered collaboration. This study presents a systematic review of the research progress and practical applications of DT-enabled HRCA. First, conceptual boundaries between HRCA and general human–robot collaboration (HRC) in manufacturing are defined. Core elements of DT-driven state perception, task planning, and constraint modeling are described. Second, four task-allocation paradigms are classified and summarized, including optimization-based, constraint satisfaction-based, data-driven intelligent, and large language model (LLM)-assisted approaches. Applicable scenarios are identified. Third, the effects of collaboration modes and interaction modalities on planning logic are analyzed. Collaboration modes are categorized as parallel, sequential, and tightly coupled. Interaction modalities are grouped into AR-based explicit interaction, implicit intention perception, and multimodal fusion. Fourth, cross-domain application characteristics and engineering bottlenecks are summarized. Target domains include precision assembly, disassembly and remanufacturing, and construction on-site operations. Finally, four core challenges are distilled, including dynamic uncertainty, multi-objective conflicts, human factor adaptation, and system integration. Four future directions are outlined: LLM-enabled adaptive planning, safety–efficiency co-optimization, personalized collaboration, and standardized integration. The proposed technology–application–challenge–outlook framework is intended to provide a theoretical reference and practical guidance for transitioning HRCA from laboratory prototypes to large-scale industrial deployment. Full article

To address the challenge of complex quality control in shared manufacturing arising from loose “partner” relationships, a quality disclosure mechanism is incorporated into a shared manufacturing supply chain. By developing a platform-led game-theoretic model, it compares four quality disclosure strategies under third-party and self-built shared manufacturing platforms, filling a theoretical gap on how quality disclosure aligns with different platform models. The findings indicate that: (1) Quality disclosure always increases platform profit, providing theoretical support for the economic incentives for platforms to promote quality transparency. (2) Under third-party shared manufacturing platforms, all manufacturers prefer unilateral disclosure by the high-quality manufacturer, indicating that this platform model naturally generates a high-quality-led signaling mechanism and reduces coordination costs. (3) Under self-built shared manufacturing platforms, strategy choice is conditional: when the disclosure level is very high, the high-quality manufacturer counter-intuitively induces the low-quality manufacturer to disclose in order to avoid excessive guarantee risk; when the market quality gap is large, bilateral disclosure is the equilibrium, jointly building market trust; when the quality gap narrows, the equilibrium returns to unilateral disclosure by the high-quality manufacturer to strengthen the quality signal.This study provides a new theoretical framework for understanding quality signaling in multi-actor collaborative settings and offers managerial insights for shared manufacturing platforms to design disclosure mechanisms and for manufacturers to choose cooperation modes. Full article

The digital carbon footprint denotes the environmental impact generated by digital technologies throughout their lifecycle. Industry 4.0 manufacturing environments rely extensively on data processing, information storage, and artificial intelligence, thereby increasing energy demand and associated carbon emissions. These conditions have intensified interest in Green AI, particularly in applications such as predictive maintenance and collaborative human–machine systems. This research investigates determinants of behavioural intention to adopt Green AI through an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model tailored to Industry 4.0 and sustainability contexts. The framework incorporates performance expectancy, Industry 4.0 eligibility, technology influence, digital manufacturing competence, sustainability conditions, Green AI recognition, and green manufacturing concern. Data were obtained from an anonymous survey of 1003 Generation Z students enrolled in technical disciplines and preparing for manufacturing-oriented careers. Relationships among constructs were analysed using partial least squares structural equation modelling (PLS-SEM). The model demonstrates strong explanatory and predictive capability. Adoption intention is primarily associated with performance expectancy, Industry 4.0 eligibility, and digital manufacturing competence, while sustainability-oriented perceptions play a contextual rather than direct behavioural role. The study offers a domain-specific empirical extension of UTAUT within pre-workforce technical education rather than proposing a new acceptance theory. The findings reflect intention formation prior to labour-market entry and require validation in operational manufacturing settings before broader generalisation. Full article

The manufacturing industry is rapidly moving toward Artificial Intelligence (AI)-driven autonomous manufacturing, which requires distributed Edge AI architectures in which intelligent devices collaborate in real time. However, the practical deployment of Edge AI is hindered by the lack of standardized, asset-centric integration across heterogeneous devices. This study presents an Asset Administration Shell (AAS)-based Edge AI framework that enables interoperable and coordinated operation among Edge devices through standardized digital asset representations and OPC UA-based communication. In the proposed framework, each Edge device is represented as an AAS-compliant digital assets, enabling both direct inter-edge coordination and centralized asset management. To demonstrate the feasibility of the framework, a functional prototype was implemented consisting of a Raspberry Pi-based Vision Inspector, an autonomous mobile robot (AMR), and an AAS-based monitoring server. Vision-based fault detection is performed directly at the Edge and transmitted in real time to the AMR and the AAS Server, enabling event-driven autonomous response and system-level monitoring. Experimental results show that real-time fault detection and response can be achieved on resource-constrained edge devices while maintaining standardized, asset-level information exchange and interoperability across heterogeneous assets. These results indicate that the AAS-based Edge AI framework provides a practical and scalable foundation for asset-centric autonomous manufacturing systems requiring both real-time operational intelligence and systematic asset management. Full article

This study proposes an integrated façade design framework that harmonizes the creative divergence of Generative AI with the economic efficiency of Design for Manufacturing and Assembly (DfMA). To address low productivity in the construction industry, a stepwise pipeline is developed, synthesizing image generation via Midjourney, automated coding using ChatGPT, and quantitative optimization. Central to this process is the Hamming Distance algorithm, which evaluates image similarity to implement core DfMA principles: standardization and simplification. The study introduces a multidimensional decision-making model utilizing Grid Size (GS), Replacement Rate (RR), and Hamming Threshold (HT) indices to visualize the trade-off between component minimization and design fidelity. This process transforms abstract 2D patterns into manufacturable geometric panels, bridging the gap between conceptual design and constructability. The results demonstrate that algorithmic optimization significantly reduces component count, contributing to potential cost savings and schedule reduction. Ultimately, this research establishes a collaborative model where architects’ qualitative insights complement AI’s quantitative analysis, enabling designers to regain agency over digital tools and realize creative visions within technical constraints. Full article

In the context of multiple overlapping uncertainties, upstream disruptions in electric vehicle supply chain networks are becoming increasingly frequent. Given the dynamic and sudden nature of disruption risks, this paper introduces a stochastic stopping model to incorporate disruption risks into resilience governance. This study constructs a differential game model for resilience governance in electric vehicle supply chain networks, involving governments, suppliers, and core manufacturers. This study proposes a dynamic resilience differential equation, which integrates resilience investment efforts. Then, this study explores optimal resilience strategies and dynamic equilibrium trajectories of resilience levels under three game models. The results indicate that optimal resilience investment efforts are negatively correlated with the effort-cost coefficients, resilience decay rates, disruption probability, and damage rate. Conversely, these efforts are positively correlated with supply chain network resilience, benefits, and the resilience influence coefficients. Disruption probability and damage rate are negatively correlated with benefits. Disruption risks distort the time preferences of governance entities, causing them to overvalue immediate gains and undervalue future returns. Finally, both supply chain resilience and total benefits reach their optimal levels under the collaborative game model. Full article

Technological progress and green, low-carbon growth are vital for sustainable economic development. Since supply chains are a major source of corporate carbon emissions and they face coordination challenges exceeding firm-level digitalization, China’s SCIAPP policy emphasizing cross-organizational green collaboration for low-carbon transformation applies to them. This study, using panel data from A-share listed companies (2013–2022), employs a difference-in-differences method to analyze how supply chain digitalization influences corporate carbon emissions within the framework of the Supply Chain Innovation and Application Pilot Program (SCIAPP). The results show that supply chain digitalization significantly lowers emissions, and the findings are robust to endogeneity tests and other robustness checks. Heterogeneity analysis indicates that firms with higher governance standards and advanced digital maturity gain the most in emission reductions, especially state-owned enterprises and manufacturing companies. Mechanism tests suggest that improvements in supply chain efficiency and increased corporate innovation drive this effect. Theoretically, the research extends the digitalization–emission relationship from individual firms to entire supply chains, proposing and confirming a dual-channel framework (efficiency and innovation) that combines transaction-cost and resource-based views. Methodologically, treating the implementation of the SCIAPP as a quasi-natural experiment yields strong causal evidence beyond mere correlations. The study highlights the importance of the SCIAPP in achieving dual carbon targets and tackling global climate challenges, providing empirical insights to help enterprises reduce emissions and promote high-quality, efficient development. Full article

As the Industry 5.0 transition unfolds, engineering education must evolve to integrate Lean manufacturing with advanced digital tools and sustainable, human-centred practices. This study presents the design and implementation of a Lean Learning Factory (LLF) that addresses this challenge by combining traditional Lean methods with technologies such as simulation, robotics, and virtual reality in a modular educational environment. At the University Centre Pitești, six hands-on projects were implemented to guide students through key concepts, including production system layout, digital assistance, sustainability, and human–robot collaboration. Through experiential learning, students engage in iterative design, data analysis, and practical validation using real equipment and software platforms. The results indicate that the LLF effectively supports the development of technical, digital, transversal, and human-centred competencies aligned with EUR-ACE^® standards. Students acquire skills in process optimisation, ergonomics, and sustainable production, while also reflecting on the ethical and social implications of automation. The study concludes that the LLF model provides a scalable and adaptable framework for engineering education. It fosters competence-based learning and prepares students for the demands of Industry 5.0. This paper contributes a replicable educational approach that blends Lean efficiency, digital transformation, and human-centred values into a cohesive learning ecosystem. Full article

Human–machine collaborative manufacturing plays an important role in emerging Industry 5.0 and smart manufacturing. However, addressing the conflict-free human–machine collaborative manufacturing problem (CHMCMP) is extremely challenging because the cooperation and conflict between humans and machines are closely intertwined. This article examines the CHMCMP within the context of integrating the flexible job-shop scheduling problem (FJSP) and the flow-shop scheduling problem (FSP). Firstly, the CHMCMP was modeled as a job-flow-shop scheduling problem (JFSP), where machine processing is an FJSP and human operation is an FSP. Our goal is to complete all manufacturing jobs while pursuing multi-objective optimization, i.e., high manufacturing performance, conflict-free human–machine collaboration, and low no-load energy consumption. Secondly, an improved artificial plant community (APC) algorithm was developed to solve the NP-hard problem. A random-pairwise single-elimination tournament system is introduced for elite selection, with a time complexity of $O\left(S\right)$ linearly correlated with the population size $\left(S\right)$, superior to the sorting-based elite selection used by most evolutionary algorithms with polynomial time complexity, i.e., $O\left(S^3\right)$ of the genetic algorithm (GA) and $O\left(S^2\right)$ of the non-dominated sorting genetic algorithm-II (NSGA-II). Thirdly, a medium-scale benchmark dataset was exploited according to a human–machine collaborative manufacturing scenario. The Gantt charts of machine processing and human operating reveal that the FJSP and the FSP are entangled and are interdependent on each other in the CHMCMP, and solving FJSP and FSP separately cannot eliminate the conflict between the two. Compared with other state-of-the-art algorithms, the APC algorithm improves the makespan by up to 11.38%, the total transfer time of humans by up to 14.09%, and the no-loaded processing energy consumption by up to 12.62% with conflict avoidance. Full article

Collaborative robots (cobots) can work cooperatively alongside humans, while contributing to task automation in industries such as manufacturing. Designed with enhanced safety features, cobots can safely assist a range of users, including those with no previous robotics experience. Despite the human-centric design of cobots, programming them can be challenging for novice operators, who may lack the skills and understanding of robotics. If left with a choice between major worker upskilling or replacement and investing in expensive and complex precision cobot positioning and object-detection systems, business owners may be reluctant to embrace cobot ownership. Furthermore, if a cobot’s primary intended tasks were simple Pick-and-Place operations, the tenuous return on investment, compared to retaining current manual processes, could make cobot adoption financially impracticable. This paper proposes a low-cost cobot control system (LCCS), an intuitive cobot solution for Pick-and-Place tasks, designed for novice cobot operators. Off-the-shelf vision-based positioning solutions, priced at around $US20,000, are typically designed to be assigned to a single cobot. The LCCS comprises a Raspberry Pi, a standard USB webcam and ArUco fiducial markers, which can easily be incorporated into a multi-cobot operation, with a combined total hardware cost of around $US100. The system scales simply and economically to support an expanding operation and it is easy to use It allows a user to specify a target pick location by positioning a portable localisation scanner upon an object to be grasped by the cobot end-effector. The scanner’s integrated webcam captures the location and orientation perspective from ArUco markers affixed to predefined positions outside the cobot workspace. By pressing a switch mounted on the scanner, the user relays the captured information, converted to 3D coordinates, to the cobot controller. Finally, the cobot’s integrated processor calculates the corresponding pose using inverse kinematics, which allows the cobot to move to the target position. Subsequent actions can be pre-programmed as required, as part of the initial system configuration. Preliminary testing indicates that the proposed system provides accurate and repeatable localisation information, with a mean positional error below 3.5 mm and a mean standard deviation less than 1.8. With a hardware investment just 0.3% of the UR5e purchase price, an easy to use, customisable, and easily scalable vision-based Pick-and-Place localisation system for cobots can be implemented. It has the potential to be a reliable and robust system that significantly lowers cobot operation barriers for novice operators by alleviating the programming requirement. By reducing the reliance on experienced programmers in a production environment, cobot tasks could be deployed more rapidly and with greater flexibility. Full article

To achieve “carbon peak and carbon neutrality” in manufacturing, this paper tackles high energy consumption in flexible job shop production by developing a low-carbon scheduling optimization model with time-of-use electricity pricing, incorporating a photovoltaic microgrid. The model minimizes makespan, carbon emissions, and costs, considering photovoltaic power uncertainty, energy storage dynamics, and time-of-use pricing. To address coupled scheduling and energy management challenges, a three-stage bilevel collaborative optimization framework is proposed, enhancing the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to develop a Collaborative MOPSO (CMOPSO). The improved algorithm features a four-layer encoding mechanism with energy factors, chaotic mapping for better global search, and adaptive mutation for population diversity. Validation using the Brandimarte benchmark demonstrates the algorithm’s robustness. Specifically, comparative experiments reveal that the proposed strategy significantly outperforms the traditional scheduling mode. While maintaining a similar makespan, the proposed method reduces production costs by 44.3% and carbon emissions by 29%. Simulations confirm that the method effectively shifts tasks to low-price periods and leverages photovoltaic energy during peak hours, supporting the manufacturing industry’s green transition. Full article

Three-dimensional (3D) anatomic modeling derived from high-resolution medical imaging, such as computed tomography (CT) and magnetic resonance imaging (MRI), has been increasingly adopted in preclinical testing and device development. This white paper describes a cardiac-specific workflow that integrates 3D printing and silicone molding for support device development and procedural simulation. Patient-derived computed tomography angiography data were segmented using FDA-cleared medical modeling software to isolate the left ventricular anatomy and were further processed in computer-aided design (CAD) to ensure accurate physiological wall thickness and structural fidelity. Material jetting 3D printing was performed on a Stratasys J750 using material distributions designed to mimic the mechanical properties of myocardium, thereby approximating myocardial compliance. In parallel, stereolithography apparatus molds were designed from the left ventricle CAD model to cast transparent, pliable left ventricular models in Sorta-Clear™ 18 silicone. The 3D-printed models preserved intricate morphological detail and were suitable for mechanical manipulation and device deployment studies, whereas silicone models offered tunable mechanical properties, transparency for visualization, and durability for repeated use. Together, these complementary modalities provided rapid manufacturing capability and application-relevant physical representation. Case-specific parameters, strengths, and limitations of both models in enhancing patient care and device testing are highlighted, with relevance to heart failure applications. Current knowledge gaps, workflow and integration challenges, and future opportunities are identified, positioning this work as a reference framework for continued innovation in anatomic modeling. Within the collaborative framework of Mayo Clinic’s Anatomic Modeling Unit and Simulation Center, this integrated modeling workflow demonstrates the value of multidisciplinary collaboration between engineers and clinicians. Clinically, these patient-specific left ventricular models may enable pre-procedural device sizing and positioning and may support simulation of mechanical circulatory support (MCS) deployment while identifying possible anatomic constraints prior to intervention. This workflow has direct applicability in advanced heart failure patients undergoing MCS support, such as the Impella axillary MCS device or the durable LVAD, with potential to reduce procedural uncertainty while reducing complications and improving peri-procedural outcomes. Additionally, these models also serve as high-accuracy educational tools, enabling trainees and multidisciplinary care teams to visualize and possibly rehearse procedural steps while gaining hands-on experience in a risk-free environment. Full article