Search Results (152)

Modern manufacturing robots must dynamically balance multiple conflicting objectives amid rapidly evolving production demands. Traditional control approaches lack the adaptability required for real-time decision-making in Industry 4.0 environments. This study presents an adaptive multi-objective reinforcement learning (MORL) framework integrating dynamic preference weighting with Pareto-optimal policy discovery for real-time adaptation without manual reconfiguration. Experimental validation employed a UR5 manipulator with RG2 gripper performing quality-aware object sorting in CoppeliaSim with realistic physics (friction μ = 0.4, Bullet engine), manipulating 12 objects across four geometric types on a dynamic conveyor. Thirty independent runs per algorithm (seven baselines, 30,000+ manipulation cycles) demonstrated +24.59% to +34.75% improvements (p < 0.001, d = 0.89–1.52), achieving hypervolume 0.076 ± 0.015 (19.7% coefficient of variation—lowest among all methods) and 95% optimal performance within 180 episodes—five times faster than evolutionary baselines. Four independent verification methods (WFG, PyMOO, Monte Carlo, HSO) confirmed measurement reliability (<0.26% variance). The framework maintains edge computing compatibility (<2 GB RAM, <50 ms latency) and seamless integration with Manufacturing Execution Systems and digital twins. This research establishes new benchmarks for adaptive robotic control in sustainable Industry 4.0/5.0 manufacturing. Full article

With the rapid advance of digital technologies, the service industry has become a key driver of sustainable economic growth and the restructuring of international trade. Drawing on value-added trade flows for five pivotal service industries—construction, air transportation, postal telecommunications, financial intermediation, and education—over 2013–2021, this study examines the spatial evolution of the global service value chain (GSVC). Using social network analysis combined with a Temporal Exponential Random Graph Model (TERGM), we assess the dynamics of the GSVC’ core–periphery structure and identify heterogeneous determinants shaping their spatial networks. The findings are as follows: (1) Exports across the five industries display an “East rising, West declining” pattern, with markedly heterogeneous magnitudes of change. (2) The construction industry is Europe-centered; air transportation exhibits a U.S.–China bipolar structure; postal telecommunications show the most pronounced “East rising, West declining” shift, forming four poles (United States, United Kingdom, Germany, China); financial intermediation contracts to a five-pole core (China, United States, United Kingdom, Switzerland, Germany); and education becomes increasingly multipolar. (3) The GSVC core–periphery system undergoes substantial reconfiguration, with some peripheral economies moving toward the core; the core expands in air transportation, while postal telecommunications exhibit strong regionalization. (4) Digital technology, foreign direct investment, and manufacturing structure promote network evolution, whereas income similarity may dampen it; the effects of economic freedom and labor-force size on spatial network restructuring differ significantly by industry. These results underscore the complex interplay of structural, institutional, and geographic drivers in reshaping GSVC networks and carry implications for fostering sustainable services trade, enhancing interregional connectivity, narrowing global development gaps, and advancing an inclusive digital transformation. Full article

Liquid microlenses and their arrays (LMLAs) have emerged as a transformative platform in adaptive optics, offering superior reconfigurability, compactness, and fast response compared to conventional solid-state lenses. This review summarizes recent progress from an application-oriented perspective, focusing on actuation mechanisms, fabrication strategies, and functional performance. Among actuation mechanisms, electric-field-driven approaches are highlighted, including electrowetting for shape tuning and liquid crystal-based refractive-index tuning techniques. The former excels in tuning range and response speed, whereas the latter enables programmable wavefront control with lower optical aberrations but limited efficiency. Notably, double-emulsion configurations, with fast interfacial actuation and inherent structural stability, demonstrate great potential for highly integrated optical components. Fabrication methodologies—including semiconductor-derived processes, additive manufacturing, and dynamic molding—are evaluated, revealing trade-offs among scalability, structural complexity, and cost. Functionally, advances in focal length tuning, field-of-view expansion, depth-of-field extension, and aberration correction have been achieved, though strong coupling among these parameters still constrains system-level performance. Looking forward, innovations in functional materials, hybrid fabrication, and computational imaging are expected to mitigate these constraints. These developments will accelerate applications in microscopy, endoscopy, AR/VR displays, industrial inspection, and machine vision, while paving the way for intelligent photonic systems that integrate adaptive optics with machine learning for real-time control. Full article

Global Value Chains (GVCs) have driven the worldwide dispersion of manufacturing but remain highly vulnerable to macro-level shocks, including financial crises, geopolitical tensions, and the COVID-19 pandemic. These shocks expose manufacturing supply chains (MSCs) to systemic risks, but limited research has explored how GVC reconfiguration mediates their impact on manufacturing supply chain resilience (MSCR). To address this gap, this study conducts an enhanced bibliometric–systematic literature review (B-SLR) of 120 peer-reviewed articles. The findings reveal that macro-level shocks induce GVC reconfigurations along geographical, value, and governance dimensions, which in turn trigger MSCR through node- and link-level mechanisms. MSCR represents a manufacturer-centered capability that enables MSCs to preserve, realign, and enhance value amid shocks. Building on these insights, this research proposes a multi-tier strategy encompassing firm-level practices, inter-firm collaborations, and policy interventions. This study outlines three key contributions. First, at the theoretical level, it embeds MSCR within a GVC framework, clarifying how GVC reconfiguration mediates SCR under macro-level shocks. Second, at the methodological level, it ensures corpus completeness through snowballing and refines bibliometric mapping with multi-dimensional visualization. Third, at the managerial level, it provides actionable guidance for firms, industry alliances, and policymakers to align MSCR strategies with the dynamics of global production networks. Full article

As climate action becomes increasingly urgent, nations and institutions worldwide seek advanced technologies for practical mitigation efforts. This study examines how agentic artificial intelligence systems capable of decision-making and learning from experience drive innovation dynamics in climate change mitigation, with a particular focus on ethical considerations during the net-zero transition. The current urgency of climate action demands advanced technologies, yet organisations struggle to effectively deploy agentic AI for climate mitigation due to unclear implementation pathways and ethical consideration. This study examines the relationships among agentic AI capabilities, innovation dynamics, and net-zero transition performance, using survey data from 340 organisations across the manufacturing, energy, and technology sectors, and analysed using structural equation modelling. Based on dynamic capabilities theory, this research proposes a novel theoretical model that examines how agentic AI drives innovation dynamics in climate change mitigation within governance frameworks that encompass transparency, accountability, and environmental justice. Results reveal significant mediation effects of innovation dynamics, dynamic capabilities, and ethical considerations, while environmental context negatively moderates innovation and ethical pathways. Findings suggest that overly restrictive ethical considerations can lead to implementation delays that undermine the urgency of climate action. This study proposes three solutions: (1) adaptive ethical protocols adjusting governance intensity based on climate risk severity, (2) pre-approved ethical templates reducing approval delays by 60%, and (3) stakeholder co-design processes building consensus during development. The research advances dynamic capabilities theory for AI contexts by demonstrating how AI-enabled sensing, seizing, and reconfiguring capabilities create differentiated pathways to climate performance. This study provides empirical validation of the responsible innovation framework, identifies asymmetric environmental contingencies, and offers evidence-based guidance for organisations implementing agentic AI for climate action. Full article

The growing complexity and scale of Cyber–Physical Systems (CPSs) have led to an increasing need for the holistic orchestration of multiple Digital Twins (DTs). Therefore, an extension to the UniTwin framework is introduced within this paper. UniTwin is a containerized, cloud-native DT framework. This extension enables the hierarchical aggregation of DTs across various abstraction levels. Traditional DT frameworks often lack mechanisms for dynamic composition at the level of entire systems. This is essential for modeling distributed systems in heterogeneous environments. UniTwin addresses this gap by grouping DTs into composite entities with an aggregation mechanism. The aggregation mechanism is demonstrated in a smart manufacturing case study, which covers the orchestration of a production line for personalized shopping cart chips. It uses modular DTs provided for each device within the production line. A System-Aggregated Digital Twin (S-ADT) is used to orchestrate the individual DTs, mapping the devices in the production line. Therefore, the production line adapts and reconfigures according to user-defined parameters. This validates the flexibility and practicality of the aggregation mechanism. This work contributes an aggregation mechanism for the UniTwin framework, paving the way for adaptable DTs for complex CPSs in domains like smart manufacturing, logistics, and infrastructure. Full article

Industrial Vision Inspection Systems (VISs) often struggle to adapt to increasing variability of modern manufacturing due to the inherent rigidity of their hardware architectures. Although the Reconfigurable Manufacturing System (RMS) paradigm was introduced in the early 2000s to overcome these limitations, designing such reconfigurable machines remains a complex, expert-dependent, and time-consuming task. This is primarily due to the lack of structured methodologies and the reliance on trial-and-error processes. In this context, this study proposes a novel theoretical framework to facilitate the design of fully reconfigurable handling systems for VISs, with a particular focus on fixture design. The framework is grounded in Model-Based Definition (MBD), embedding semantic information directly into the 3D CAD models of the inspected product. As an additional contribution, a general hardware architecture for the inspection of axisymmetric components is presented. This architecture integrates an anthropomorphic robotic arm, Numerically Controlled (NC) modules, and adaptable software and hardware components to enable automated, software-driven reconfiguration. The proposed framework and architecture were applied in an industrial case study conducted in collaboration with a leading automotive half-shaft manufacturer. The resulting system, implemented across seven automated cells, successfully inspected over 200 part types from 12 part families and detected more than 60 defect types, with a cycle below 30 s per part. Full article

Supervisory control theory (SCT), based on Petri nets, offers a robust framework for modeling and controlling discrete-event systems but faces significant challenges in scalability, expressiveness, and practical implementation. This paper introduces General-purpose Petri Net Simulator and Real-Time Controller (GPenSIM), a MATLAB version 24.1.0.2689473 (R2024a) Update 6-based modular Petri net framework, as a novel solution to these limitations. GPenSIM leverages modular decomposition to mitigate state-space explosion, enabling parallel execution of weakly coupled Petri modules on multi-core systems. Its programmable interfaces (pre-processors and post-processors) extend classical Petri nets’ expressiveness by enforcing nonlinear, temporal, and conditional constraints through custom MATLAB scripts, addressing the rigidity of traditional linear constraints. Furthermore, the integration of GPenSIM with MATLAB facilitates real-time control synthesis, performance optimization, and seamless interaction with external hardware and software, bridging the gap between theoretical models and industrial applications. Empirical studies demonstrate the efficacy of GPenSIM in reconfigurable manufacturing systems, where it reduced downtime by 30%, and in distributed control scenarios, where decentralized modules minimized synchronization delays. Grounded in systems theory principles of interconnectedness, GPenSIM emphasizes dynamic relationships between components, offering a scalable, adaptable, and practical tool for supervisory control. This work highlights the potential of GPenSIM to overcome longstanding limitations in SCT, providing a versatile platform for both academic research and industrial deployment. Full article

Mass customization (MC) has become a pivotal manufacturing strategy for addressing the growing demand for personalized products without compromising cost efficiency and scalability. The emergence of Industry 4.0 (I4.0) has further expanded the potential of MC by enabling intelligent, flexible, and interconnected production systems. This paper presents a systematic literature review covering the period from 2011 to 2024, aimed at examining how I4.0 technologies influenced the conceptual evolution, technological enablers, and supply chain implications of MC. A total of 3441 publications were retrieved from Scopus and analyzed using a combination of bibliometric mapping and qualitative synthesis. The review identifies three primary research streams: (1) MC conceptual frameworks and performance metrics, (2) enabling technologies and methods across the product lifecycle, and (3) supply chain strategies tailored to MC environments. Key enablers such as product modularity, customer co-design platforms, additive manufacturing, and reconfigurable production systems are discussed, along with barriers related to complexity, integration challenges, and sustainability trade-offs. The study highlights a gradual convergence toward mass personalization, supported by real-time data, artificial intelligence, and predictive analytics. The findings offer a structured understanding of MC in the I4.0 context and point toward future research opportunities involving digital twin integration, cross-disciplinary implementation models, and sustainability-driven customization frameworks. Full article

This paper presents a novel method for centralized robotic swarm control that integrates path planning and task allocation subsystems. A swarm of agents is managed using various evaluation methods to assess performance. A feedforward neural network was developed to assign tasks to swarm agents in real time by predicting a suitability score. For centralized swarm planning, a hybrid algorithm combining Rapidly Exploring Random Tree (RRT) and Artificial Potential Field (APF) planners was implemented, incorporating a Multi-Agent Pathfinding (MAPF) solution to resolve simultaneous collisions at intersections. Additionally, experimental hardware using differential-drive, ArUco-tracked agents was developed to refine and demonstrate the proposed control solution. This paper specifically focuses on the swarm system design for applications in swarm reconfigurable manufacturing systems. Therefore, performance was evaluated on tasks that resemble such processes. Full article

Within China’s “Central China Rising” strategy, urban tourismification operates as a production mode that reconfigures spatial, economic, and ecological systems—mirroring global overtourism challenges seen in Barcelona and Venice, where rapid infrastructure development often prioritizes economic gains over ecological resilience (cf. Lines 43–46). This study examines 80 central Chinese cities (2010–2021), proposing the Urban Tourismification–Transportation Quality–Ecological Resilience System (UTTES) framework. Using entropy weighting, improved coupling coordination degree (CCD), GM (1,1) forecasting, and spatial Durbin models, we analyze coordination relationships, driving factors, and mechanisms. Key findings reveal the following: (1) UTTES coordination peaked in 2019 (pre-COVID), showing a spatial “center-periphery” gradient with provincial capitals leading. (2) Projections indicate transportation efficiency as a critical bottleneck—most cities will achieve good coordination post-2026. (3) Economic activity, social restructuring, and policy support drive the system, with spatial spillovers creating dual-path mechanisms (economic growth vs. manufacturing/environmental barriers). The UTTES framework advances a replicable methodology for diagnosing Tourism–Transportation–Ecology synergies in rapidly developing regions, integrating multidimensional indicators to balance environmental governance and tourism dynamics. Full article

As Industry 5.0 progresses, the demand for zero-touch configuration in industrial automation and smart manufacturing is increasing. This paper proposes a dynamic configuration management framework for Time-Sensitive Networking (TSN), aiming to address the challenges of flexibility and adaptability in dynamic network environments. A zero-touch configuration model is presented for TSN by incorporating a Delay-Aware Shortest Path Search (DASPS) algorithm to improve scheduling success rates. Simulation results demonstrate the ability of the framework to reconfigure networks within 2.67 milliseconds. The DASPS algorithm achieves a scheduling success rate of 70.22% for 1000 TSN flows, in contrast to only 22.23% achieved by the Shortest Path Search (SPS) algorithm. The proposed model effectively adapts to dynamic network changes, guaranteeing real-time data transmission. To further evaluate system adaptability, path entropy is introduced as a metric to quantitatively assess the balance of scheduling outcomes under topological changes. In the event of link failures, path entropy experiences a sharp decline but rapidly recovers after reconfiguration, demonstrating the system’s strong self-healing capability. Full article

Integrated process planning and scheduling (IPPS) is an intricate and vital issue in smart manufacturing, requiring the coordinated optimization of both process plans and production schedules under multiple resource and precedence constraints. This paper presents a novel optimization framework, symmetry-aware adaptive Ant Colony Optimization (SA-AACO), designed to resolve key limitations in existing metaheuristic approaches. The proposed method introduces three core innovations: (1) a symmetry-awareness mechanism to eliminate redundant solutions arising from symmetrically equivalent configurations; (2) an adaptive pheromone-updating strategy that dynamically balances exploration and exploitation; and (3) a dynamic idle time penalty system, integrated with time window-based machine selection. Benchmarked across ten IPPS scenarios, SA-AACO achieves a superior makespan in 9/10 cases (e.g., 29.1% improvement over CCGA in Problem 1) and executes 18-part processing within 30 min. While MMCO marginally outperforms SA-AACO in Problem 10 (makespan: 427 vs. 483), SA-AACO’s consistent dominance across diverse scales underscores the feasibility of its application in industry to balance quality and efficiency. By unifying symmetry handling and adaptive learning, this work advances the reconfigurability of IPPS solutions for dynamic industrial environments. Full article

The technological advances in internet of things (IoT) devices have raised the demand for cost-efficient and sustainable energy sources. Piezoelectric energy harvesters (PEHs) are promising low-cost and eco-friendly energy sources but require robust power management circuits (PMCs) for voltage conversion and regulation. This work presents a complementary metal–oxide–semiconductor (CMOS)-based PMC, integrating a reconfigurable AC-DC rectifier and a low-dropout (LDO) voltage regulator designed using 0.18 µm Taiwan semiconductor manufacturing company (TSMC) CMOS technology. This design includes an intermediate coupling stage to reduce voltage drop and improve the transfer efficiency of the PMC. In addition, we develop numerical simulations of the PMC performance, achieving a voltage conversion efficiency (VCE) between 72.8% and 43.21% using input voltages from 0.7 V to 2.8 V with a 50 kΩ load resistance. Compared to previous designs, the proposed circuit demonstrates improved stability, reduced area (66.28 mm²), and extended operating voltage range, allowing its potential application for ultra-low-power IoT nodes. This PMC contributes to the development of autonomous systems with reduced battery dependency and enhanced sustainability. Full article

Millimeter-wave signals experience substantial path loss when penetrating common building materials, hindering seamless indoor coverage from outdoor networks. To address this limitation, we present the 28-GHz “Meta-Window”, a mass-producible, visible transparent device designed to enhance millimeter-wave signal focusing. Fabricated via metal sputtering and etching on a standard soda-lime glass substrate, the meta-window incorporates subwavelength metallic structures arranged in a rotating pattern based on the Pancharatnam–Berry phase principle, enabling 0–360$°$ phase control within the 25–32 GHz frequency band. A 210 mm × 210 mm prototype operating at 28 GHz was constructed using a 69 × 69 array of metasurface unit cells, leveraging planar electromagnetic lens principles. Experimental results demonstrate that the meta-window achieves greater than 20 dB signal focusing gain between 26 and 30 GHz, consistent with full-wave electromagnetic simulations, while maintaining up to 74.93% visible transmittance. This dual transparency—for both visible light and millimeter-wave frequencies—was further validated by a communication prototype system exhibiting a greater than 20 dB signal-to-noise ratio improvement and successful demodulation of a 64-QAM single-carrier signal (1 GHz bandwidth, 28 GHz) with an error vector magnitude of 4.11%. Moreover, cascading the meta-window with a reconfigurable reflecting metasurface antenna array facilitates large-angle beam steering; stable demodulation (error vector magnitude within 6.32%) was achieved within a ±40$°$ range using the same signal parameters. Compared to conventional transmissive metasurfaces, this approach leverages established glass manufacturing techniques and offers potential for direct building integration, providing a promising solution for improving millimeter-wave indoor penetration and coverage. Full article