Search Results (88)

The global clean energy transition requires power conversion technologies that combine high efficiency, operational flexibility, and reduced environmental impact over their entire service life. Solid-state transformers (SSTs) have emerged as a promising alternative to conventional line-frequency transformers, offering bidirectional power flow, high-frequency isolation, and advanced control capabilities that support renewable integration and electrified infrastructures. This paper presents a comparative life cycle assessment (LCA) of conventional transformers and SSTs across representative power-system applications, including residential and industrial distribution networks, electric vehicle fast-charging infrastructure, and transmission–distribution interface substations. The analysis follows a cradle-to-grave approach and is based on literature-derived LCA data, manufacturer specifications, and harmonized engineering assumptions applied consistently across all case studies. The results show that, under identical assumptions, SST-based solutions are associated with indicative lifecycle CO₂ emission reductions of approximately 10–30% compared to conventional transformers, depending on power rating and operating profile (≈90–1000 t CO₂ over 25 years across the four cases). These reductions are primarily driven by lower operational losses and reduced material intensity, while additional system-level benefits arise from enhanced controllability and compatibility with renewable-rich and hybrid AC/DC grids. The study also identifies key challenges that influence the sustainability performance of SSTs, including higher capital cost, thermal management requirements, and the long-term reliability of power-electronic components. Overall, the results indicate that SSTs represent a relevant enabling technology for future low-carbon power systems, while highlighting the importance of transparent assumptions and lifecycle-oriented evaluation when comparing emerging grid technologies. Full article

High variability in multi-product manufacturing environments and rapidly changing customer demands make decentralized coordination of work-in-process (WIP) and production resources increasingly important. However, the intrinsic rigidity of conventional centralized and monolithic manufacturing execution systems (MESs) renders them unsuitable for such highly dynamic environments. To address this limitation, this study proposes an agent-based distributed, decentralized MES architecture. The manufacturing execution process is realized through collaboration among constituent agents based on an employment network (EmNet). Specifically, three types of agents are introduced: WIPAgents (representing WIPs), PAgents (representing processing resources), and MHAgents (representing material-handling resources). Collaboration among agents (e.g., collaborator discovery, partner selection, and data sharing/exchange) is facilitated by a data-space-based collaboration platform which was introduced in our prior work. To validate the proposed architecture, we built a digital-twin-based simulation testbed and conducted simulation experiments. The experimental results confirm the validity and operational feasibility of the proposed architecture. Full article

A cascade deep learning approach is proposed for optimizing the design and control of a dual-frequency induction heating system used in semiconductor manufacturing. The system is composed of two independent power inductors, fed at different frequencies, to achieve a homogeneous temperature profile along a graphite susceptor surface, crucial for enhancing layer quality and integrity. The optimization process considers both electrical (current magnitudes and frequencies) and geometrical parameters of the coils, which influence the power penetration and subsequent temperature distribution within the graphite disk. A two-step procedure based on deep neural networks (DNNs) is employed. The first step, namely optimal design, identifies the optimal operating frequencies and geometrical parameters of the two coils. The second step, namely optimal control, determines the optimal current magnitudes. The DNNs are trained using a database generated through finite element (FE) analysis. This deep learning-based cascade approach reduces computational time and multiphysics simulations compared to classical methods by reducing the dimensionality of parameter mapping. Therefore, the proposed method proves to be effective in solving high-dimensional multiphysics inverse problems. From the application point of view, achieving thermal uniformity (±7% fluctuation at 1100 °C) improves layer quality, increases efficiency, and reduces operating costs of epitaxy reactors. Full article

The advancement of adaptable industrial robots in intelligent manufacturing is hindered by the inefficiency of traditional motion planning methods in high-dimensional spaces. Therefore, a Dempster–Shafer evidence theory-based hybrid motion planner is proposed, in which a probabilistic sampling network (PSNet) and an enhanced artificial potential field (EAPF) cooperate with each other to improve the planning performance. The PSNet architecture comprises two modules: a motion planning module (MPM) and a fusion sampling module (FSM). The MPM utilizes sensor data alongside the robot’s current and target configurations to recursively generate diverse multimodal distributions of the next configuration. Based on the distribution information, the FSM was used as a decision-maker to ultimately generate globally connectable paths. Moreover, the FSM is equipped to correct collision path points caused by network inaccuracies through Gaussian resampling. Simultaneously, an augmented artificial potential field with a dynamic rotational field is deployed to repair local paths when worst-case collision scenarios occur. This collaborative strategy harmoniously unites the complementary strengths of both components, thereby enhancing the overall resilience and adaptability of the motion planning system. Experiments were conducted in various environments. The results demonstrate that the proposed method can quickly find directly connectable paths in diverse environments while reliably avoiding sudden obstacles. Full article

Ensuring optimal adhesive curing during plywood and LVL (Layered Veneer Lumber) hot pressing requires accurate knowledge of internal temperature distribution, which is often difficult to assess using conventional surface-based measurements. This study introduces a custom-developed multi-layer smart sensor network capable of in situ, real-time temperature profiling across LVL layers during industrial hot pressing. The system integrates miniature embedded sensors and proprietary data acquisition software, enabling the simultaneous multi-point monitoring of thermal dynamics with a high temporal resolution. Experiments were performed on LVL panels of varying thicknesses, applying industry-standard pressing schedules derived from conventional calculation rules. Despite adherence to prescribed pressing times, results reveal significant core temperature deficits in thicker panels, potentially compromising adhesive gelation and overall bonding quality. These findings underline the need to revisit the pressing time determination for thicker products and demonstrate the potential of advanced sensing technologies to support adaptive process control. The proposed approach contributes to smart manufacturing and the remote-like monitoring of internal thermal states, providing valuable insights for enhancing product performance and industrial process efficiency. Full article

The surface quality of optical components plays a decisive role in advanced imaging, precision manufacturing, and high-power laser systems, where even defects can induce abnormal scattering and degrade system performance. Addressing the limitations of conventional single-view inspection methods, this study presents a panoramic multi-angle scattered light field acquisition approach integrated with deep learning-based recognition. A hemispherical synchronous imaging system is designed to capture complete scattered distributions from surface defects in a single exposure, ensuring both structural consistency and angular completeness of the measured data. To enhance the interpretation of complex scattering patterns, we develop a tailored lightweight network, SFD-YOLO, which incorporates the PSimam attention module for improved salient feature extraction and the Efficient_Mamba_CSP module for robust global semantic modeling. Using a simulated dataset of multi-width scratch defects, the proposed method achieves high classification accuracy with strong generalization and computational efficiency. Compared to the baseline YOLOv11-cls, SFD-YOLO improves Top-1 accuracy from 92.5% to 95.6%, while reducing the parameter count from 1.54 M to 1.25 M and maintaining low computational cost (Flops 4.0G). These results confirm that panoramic multi-angle scattered imaging, coupled with advanced neural architectures, provides a powerful and practical framework for optical surface defect detection, offering valuable prospects for high-precision quality evaluation and intelligent defect inversion in optical inspection. Full article

In dynamic and open cloud service processes, particularly in distributed networked manufacturing environments, the complex and volatile manufacturing landscape introduces numerous uncertainties and disturbances. This paper addresses the common issue of cloud resource connection interruptions by proposing a path substitution strategy based on alternative service routes. By integrating agent-based simulation and complex network methodologies, a simulation model for evaluating the robustness of cloud manufacturing service systems is developed, enabling dynamic simulation and quantitative decision-making for the proposed robustness enhancement strategies. First, a hybrid modeling approach for cloud manufacturing service systems is proposed to meet the needs of robustness analysis. The specific construction of the hybrid simulation model is achieved using the AnyLogic 8.7.4 simulation software and Java-based secondary development techniques. Second, a complex network model focusing on cloud manufacturing resource entities is further constructed based on the simulation model. By combining the two models, two-dimensional robustness evaluation indicators—comprising performance robustness and structural robustness—are established. Then, four types of edge attack strategies are designed based on the initial topology and recomputed topology. To ensure system operability after edge failures, a path substitution strategy is proposed by introducing redundant routes. Finally, a case study of a cloud manufacturing project is conducted. The results show the following: (1) The proposed robustness evaluation model fully captures complex disturbance scenarios in cloud manufacturing, and the designed simulation experiments support the evaluation and comparative analysis of robustness improvement strategies from both performance and structural robustness dimensions. (2) The path substitution strategy significantly enhances the robustness of cloud manufacturing services, though its effects on performance and structural robustness vary across different disturbance scenarios. Full article

Modern industrial ecosystems, particularly those embracing Industry 4.0, increasingly depend on coherent optical networks operating at 400 Gbps and beyond. These high-capacity infrastructures, coupled with advanced digital signal processing and phase-sensitive detection, enable real-time data exchange for automated manufacturing, robotics, and interconnected factory systems. However, they introduce multilayer security challenges—ranging from hardware synchronization gaps to protocol overhead manipulation. Moreover, the rise of large-scale quantum computing intensifies these threats by potentially breaking classical key exchange protocols and enabling the future decryption of stored ciphertext. In this paper, we present a systematic vulnerability analysis of coherent optical networks that use OTU4 framing, Media Access Control Security (MACsec), and 400G ZR+ transceivers. Guided by established risk assessment methodologies, we uncover critical weaknesses affecting management plane interfaces (e.g., MDIO and I²C) and overhead fields (e.g., Trail Trace Identifier, Bit Interleaved Parity). To mitigate these risks while preserving the robust data throughput and low-latency demands of industrial automation, we propose a post-quantum security framework that merges spectral phase masking with multi-homodyne coherent detection, strengthened by quantum key distribution for key management. This layered approach maintains backward compatibility with existing infrastructure and ensures forward secrecy against quantum-enabled adversaries. The evaluation results show a substantial reduction in exposure to timing-based exploits, overhead field abuses, and cryptographic compromise. By integrating quantum-safe measures at the optical layer, our solution provides a future-proof roadmap for network operators, hardware vendors, and Industry 4.0 stakeholders tasked with safeguarding next-generation manufacturing and engineering processes. Full article

Against the backdrop of accelerating transformation in traditional mechanical manufacturing toward intelligent production models integrating mechanical, electronic, and information technologies, coupled with increasing demands for mass customization, conventional machining methods are proving inadequate to meet modern manufacturing requirements. To address these challenges, this study proposes a knowledge-based intelligent process planning system. First, to address the heterogeneity issues in knowledge aggregation during machining processes, a process knowledge model comprising three sub-models was designed. Using ontological analysis methods with OWL language, inter-model relationships were formally expressed, achieving structured knowledge representation. Furthermore, to meet the system’s substantial knowledge demands, a MySQL-based knowledge framework was developed, enabling distributed storage and the intelligent retrieval of process planning knowledge. Second, to overcome limitations like low openness and decision-making rigidity in traditional process planning, a hybrid reasoning mechanism was proposed: on the one hand, an instance and rule-based reasoning system ensures adaptability to parameter variations; on the other hand, Generative Adversarial Networks are introduced to transcend the completeness limitations of traditional knowledge reasoning, enabling the dynamic evolution of process knowledge. Finally, the intelligent process planning system was implemented in Python on the VSCode platform. Validation via typical turning cases demonstrates the system’s autonomous process planning and execution capabilities. Full article

Polyphenylene sulfide (PPS)-based separators have garnered significant attention as high-performance components for next-generation lithium-ion batteries (LIBs), driven by their exceptional thermal stability (>260 °C), chemical inertness, and mechanical durability. This review comprehensively examines advances in PPS separator design, focusing on two structurally distinct categories: porous separators engineered via wet-chemical methods (e.g., melt-blown spinning, electrospinning, thermally induced phase separation) and nonporous solid-state separators fabricated through solvent-free dry-film processes. Porous variants, typified by submicron pore architectures (<1 μm), enable electrolyte-mediated ion transport with ionic conductivities up to >1 mS·cm⁻¹ at >55% porosity, while their nonporous counterparts leverage crystalline sulfur-atom alignment and trace electrolyte infiltration to establish solid–liquid biphasic conduction pathways, achieving ion transference numbers >0.8 and homogenized lithium flux. Dry-processed solid-state PPS separators demonstrate unparalleled thermal dimensional stability (<2% shrinkage at 280 °C) and mitigate dendrite propagation through uniform electric field distribution, as evidenced by COMSOL simulations showing stable Li deposition under Cu particle contamination. Despite these advancements, challenges persist in reconciling thickness constraints (<25 μm) with mechanical robustness, scaling solvent-free manufacturing, and reducing costs. Innovations in ultra-thin formats (<20 μm) with self-healing polymer networks, coupled with compatibility extensions to sodium/zinc-ion systems, are identified as critical pathways for advancing PPS separators. By addressing these challenges, PPS-based architectures hold transformative potential for enabling high-energy-density (>500 Wh·kg⁻¹), intrinsically safe energy storage systems, particularly in applications demanding extreme operational reliability such as electric vehicles and grid-scale storage. Full article

Many current 1 × 2 splitter couplers based on multimode interference (MMI) face difficulties such as significant back reflection and limited flexibility in waveguide segmentation at the output, which necessitate the addition of transitional structures like tapered waveguides or S-Bends. These limitations reduce their effectiveness as photonic data-center applications, where precise waveguide configurations are crucial. To address these challenges, we propose a novel nanoscale 1 × 2 angled multimode interference (AMMI) power splitter with silicon nitride (SiN) buried core and silica cladding. The innovative angled light path design improved performance by minimizing back reflections back to the source and by providing greater flexibility of waveguide interconnections, making the splitter more adaptable for data-center applications. The SiN core was selected due to its lower refractive index contrast with silica compared to silicon, which helps further reduce back reflection. The dimensions of the splitter were optimized using full vectorial beam propagation method (FV-BPM), finite-difference time domain (FDTD), and multivariable optimization scanning tool (MOST) simulations to support transmission across the O-band. Our proposed device demonstrated excellent performance, achieving an excess loss of 0.22 dB and an imbalance of <0.01 dB at the output ports at an operational wavelength of 1.31 µm. The total device length is 101 µm with a thickness of 0.4 µm. Across the entire O-band range (1260–1360 nm), the performance of the splitter presented excess loss of up to 1.57 dB and an imbalance of up to 0.05 dB. Additionally, back reflections at the operational wavelength were measured at −40.96 dB and up to −39.67 dB over the O-band. This silicon-on-insulator (SOI) complementary metal-oxide semiconductor (CMOS) compatible AMMI splitter demonstrates high tolerance for manufacturing deviations due to its geometric layout, dimensions, and material selection. Furthermore, the proposed splitter is well-suited for use in O-band transceiver systems and can enhance data-center optical networks by supporting high-speed, low-loss data transmission. The compact design and CMOS compatibility make this device ideal for integrating into dense, high-performance computing environments, ensuring reliable signal distribution and minimal power loss. The splitter can support multiple communication channels, thus enhancing bandwidth and scalability for next-generation data-center infrastructures. Full article

Conventional shotcrete systems face critical limitations in adverse geological environments, including a delayed strength development (<5 MPa at 3 h), excessive rebounds (15–25%), and permeable macropore networks (>50 μm), often resulting in support failure for deeply buried tunnels. To address these challenges, this study systematically investigates the mechanical properties and pore characteristics of a manufactured sand-based sprayed UHPC at different spraying positions under simulated tunnel conditions. Our results demonstrate that the high-pressure air (0.8 MPa) driven spraying process optimizes its pore distribution, reducing large pores (>10 μm) and increasing harmless pores (<100 nm). Furthermore, the sprayed UHPC incorporating manufactured sand derived from tunnel slag not only maintains a 28-day compressive strength of 110.9 MPa but also reduces material costs and enhances sustainability. Field tests validate its low rebound rate (<5%) and rapid strength development (achieving a compressive strength of 30 MPa within 1 day), confirming its adaptability to complex geological conditions such as high-stress zones, thereby providing a novel method for support in complex geological conditions. Full article

The evolution of Protection and Control (P&C) systems has developed though analogue and digital generations, and is presently advancing towards the utilization of Virtualization of Protection, Automation and Control environments (VPAC). This article focuses on redefining the features of traditional and modern P&C systems, Centralized Protection Automation and Control (CPAC), and VPAC, focusing on the integration of Intelligent Electronic Devices (IEDs) with secure communication that is time-effective in the centralized distribution of power and prevention of network vulnerability. Though standards such as IEC 61850-9-2 LE have been adopted, the actualization of full interoperability between diverse IED manufacturers remains elusive. With the digitization of technologies, P&C systems are naturally transitioning to virtual environments, with timing precision, redundancy and security being imperative. Latency and resource management and allocation in VPAC systems are considerable global issues. This paper discusses the issues of maintaining low operational performance in virtual substation environments while satisfying the requirements for performance in real time. The impacts of large volumes of data and artificial intelligence on the management of the grid are studied, and AI-based analytics that predict system failures and automatically change load flows are shown, as they have the potential to increase the flexibility and stability of the grid. The use of big data enables electric power utilities to enhance their protection systems, anticipate disturbances and improve energy management methods. The paper presents a comparative analysis between traditional P&C and its virtualized counterparts, with strong emphasis placed on the flexibility and scaling of VPAC resources. Full article

The pursuit of energy security has become one of the most important challenges facing modern societies worldwide. The increase in energy consumption and the need to promote sustainability puts pressure on power generation systems. In this context, renewable energy sources have become a favorable option to improve both energy security and sustainability while promoting the use of domestic energy sources. The supply chain is an optimized methodology that includes all necessary activities to bring a product to the final consumer. Traditionally applied in the manufacturing industry, recent evidence shows its successful implementation in various renewable energy sectors. In this work, a novel methodology based on a supply chain was designed to evaluate the feasibility of mini wind power systems in urban areas in an integrated and measurable manner. The main contribution lies in the integration of several different approaches, currently recognized as the most relevant factors for determining the viability of wind energy projects. A five-link supply chain model was proposed, which includes an evaluation of wind potential, supplier network, project technical assessment, customer distribution, and equipment final disposal. Specific metric indicators for each link were developed to evaluate technical, legislative, and social considerations. The methodology was applied in a case study in the city of Mexicali, Mexico. The findings show that although wind as a resource remains the most important factor, local government policies that promote the use of renewable energy, the supplier’s availability, qualified human resources, and spare parts are also of equivalent significance for the successful implementation of mini wind power systems. Full article

Background: The emergence of more than 40 new infectious diseases since the 1980s has emerged as a serious global health concern, many of which are zoonotic. In response, many international organizations, including the US Centers for Disease Control and Prevention (CDC), the World Health Organization (WHO), and the European Center for Disease Prevention and Control (ECDC), have developed strategies to combat these health threats. The need for rapid vaccine development has been highlighted by Coronavirus disease 2019 (COVID-19), and mRNA technology has shown promise as a platform. While the acceleration of vaccine development has been successful, concerns have been raised about the technical limits, safety, supply, and distribution of vaccines. Objective: This study analyzes the status of vaccine platform development in global pandemics and explores ways to respond to future pandemic crises through an overview of the roles of international organizations and their support programs. It examines the key roles and partnerships of international organizations such as the World Health Organization (WHO), vaccine research and development expertise of the Coalition for Epidemic Preparedness Innovations (CEPI), control of the vaccine supply chain and distribution by the Global Alliance for Vaccines and Immunization (GAVI), and technology transfer capabilities of the International Vaccine Institute (IVI) in supporting the development, production, and supply of vaccine platform technologies for pandemic priority diseases announced by WHO and CEPI and analyzes their vaccine support programs and policies to identify effective ways to rapidly respond to future pandemics caused by emerging infectious diseases. Methods: This study focused on vaccine platform technology and the key roles of international organizations in the pandemic crisis. Literature data on vaccine platform development was collected, compared, and analyzed through national and international literature data search sites, referring to articles, journals, research reports, publications, books, guidelines, clinical trial data, and related reports. In addition, the websites of international vaccine support organizations, such as WHO, CEPI, GAVI, and IVI, were used to examine vaccine support projects, initiatives, and collaborations through literature reviews and case study methods. Results: The COVID-19 pandemic brought focus on the necessity for developing innovative vaccine platforms. Despite initial concerns, the swift integration of cutting-edge development technologies, mass production capabilities, and global collaboration have made messenger RNA (mRNA) vaccines a game-changing technology. As a result of the successful application of novel vaccine platforms, it is important to address the remaining challenges, including technical limits, safety concerns, and equitable global distribution. To achieve this, it is essential to review the regulatory, policy, and support initiatives that have been implemented in response to the COVID-19 pandemic, with particular emphasis on the key stages of vaccine development, production, and distribution, to prepare for future pandemics. An analysis of the status of vaccine development for priority pandemic diseases implies the need for balanced vaccine platform development. Also, international organizations such as WHO, CEPI, GAVI, and IVI play key roles in pandemic preparedness and the development and distribution of preventive vaccines. These organizations collaborated to improve accessibility to vaccines, strengthen the global response to infectious diseases, and address global health issues. The COVID-19 pandemic response demonstrates how the synergistic collaboration of WHO’s standardized guidelines, CEPI’s vaccine research and development expertise, GAVI’s control of the vaccine supply chain and distribution, and IVI’s technology transfer capabilities can be united to create a successful process for vaccine development and distribution. Conclusions: In preparation for future pandemics, a balanced vaccine platform development is essential. It should include a balanced investment in both novel technologies such as mRNA and viral vector-based vaccines and traditional platforms. The goal is to develop vaccine platform technologies that can be applied to emerging infectious diseases efficiently and increase manufacturing and distribution capabilities for future pandemics. Moreover, international vaccine support organizations should play key roles in setting the direction of global networking and preparing for international vaccine support programs to address the limitations of previous pandemic responses. As a result, by transforming future pandemic threats from unpredictable crises to surmountable challenges, it is expected to strengthen global health systems and reduce the social and economic burden of emerging infectious diseases in the long term. Full article