IET Collaborative Intelligent Manufacturing最新文献_第2页

An Adaptive Pipe Inspection Robot for Complex Pipeline Navigation 一种适应复杂管道导航的管道检测机器人

IF 3.3

IET Collaborative Intelligent Manufacturing Pub Date : 2026-07-29 DOI: 10.1049/cim2.70072

Md. Nahidul Alam, Md. Zahid Hasan Buiyan, Mohammad Sayem, Md. Abdullah Al Hasan Anik

{"title":"An Adaptive Pipe Inspection Robot for Complex Pipeline Navigation","authors":"Md. Nahidul Alam, Md. Zahid Hasan Buiyan, Mohammad Sayem, Md. Abdullah Al Hasan Anik","doi":"10.1049/cim2.70072","DOIUrl":"https://doi.org/10.1049/cim2.70072","url":null,"abstract":"The most common approach for transporting gases and liquids depends on pipelines. However, most of the time, these pipelines are affected by leakage and corrosion. Therefore, pipeline inspection is essential for proper transportation. People face hazardous situations when inspecting pipelines, where a robot can be a good solution. This paper presents an advanced pipe inspection robot for investigating complex pipeline infrastructures. The robot's wall press mechanism allows seamless navigation across pipe sizes starting from 250 mm up to 450 mm. Its unique design includes a comprehensive sensor suite for high-resolution imaging, environmental monitoring and gas detection. The robot's adaptive control system allows it to negotiate sharp bends and abrupt changes in pipe dimensions whilst maintaining stability and motion precision. The robot's kinematic analysis is based on rigorous mathematical modelling that balances energy consumption and navigational accuracy. It also uses a multisensor feedback loop integrated with real time data transmission systems for continuous environmental monitoring and adaptive response capabilities. The robot is well-suited for deployment in challenging environments, such as oil and gas pipelines, water distribution systems and sewage networks, offering a robust scalable solution for modern infrastructure inspection. The research represents a leap forward in autonomous pipeline inspection, combining mechanical innovation with sensor-based intelligence.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Order Picking Systems in the Transition to Industry 5.0: A Systematic Review of Human-Robot Collaboration 向工业5.0过渡的拣货系统：人机协作的系统回顾

IF 3.3

IET Collaborative Intelligent Manufacturing Pub Date : 2026-07-27 DOI: 10.1049/cim2.70071

David Montemurri, Diego Gabriel Rossit, Daniel Alejandro Rossit

{"title":"Order Picking Systems in the Transition to Industry 5.0: A Systematic Review of Human-Robot Collaboration","authors":"David Montemurri, Diego Gabriel Rossit, Daniel Alejandro Rossit","doi":"10.1049/cim2.70071","DOIUrl":"https://doi.org/10.1049/cim2.70071","url":null,"abstract":"This systematic review examines how the combined pressures of e-commerce growth and workforce constraints are reshaping order picking and fulfilment systems, with particular attention to the emergence of Human-Robot Collaboration (HRC) within automated and hybrid warehouse environments aligned with the Industry 5.0 paradigm. Focusing on automated and hybrid configurations, the review synthesises prior studies on Order Picking Systems (OPS) design, automation technologies and operational performance, analysing how collaborative human-robot systems influence flexibility, resilience and human-centred work design in modern fulfilment systems. The literature is analysed along three complementary dimensions: methodological orientation (optimization, simulation, empirical, analytical and conceptual), sociotechnical system design and operational implications for resilience, sustainability and human-centricity. The results reveal a mature body of optimization- and simulation-based research targeting efficiency objectives such as throughput and travel reduction, contrasted with a comparatively limited set of empirical studies addressing human factors, learning dynamics and long-term workforce outcomes. Although Industry 5.0 provides strong conceptual grounding for human-centric and resilient systems, its practical implementation remains fragmented. The review identifies persistent challenges related to market and operational complexity, technological limitations and system resilience and sustainable work design. By consolidating these findings, the article highlights key research gaps and outlines future directions towards integrated sociotechnical frameworks capable of jointly optimising operational performance, human wellbeing and system adaptability in next-generation fulfilment environments.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Unified Stochastic Fractional Routing Algorithm for Automated Guided Vehicles With Exponential Reliability Risks 具有指数可靠性风险的自动引导车辆的统一随机分式路径算法

IF 3.3

IET Collaborative Intelligent Manufacturing Pub Date : 2026-07-20 DOI: 10.1049/cim2.70070

Hamed Fazlollahtabar

{"title":"A Unified Stochastic Fractional Routing Algorithm for Automated Guided Vehicles With Exponential Reliability Risks","authors":"Hamed Fazlollahtabar","doi":"10.1049/cim2.70070","DOIUrl":"https://doi.org/10.1049/cim2.70070","url":null,"abstract":"Automated Guided Vehicles (AGVs) operating in flexible manufacturing systems are exposed to uncertain travel conditions and random failures that significantly affect system performance. Traditional AGV routing methods often rely on additive cost models or deterministic reliability measures, which may fail to capture the probabilistic nature of failure events and the trade-off between efficiency and survivability. This paper proposes a unified stochastic fractional routing algorithm for AGV networks in which arc failures are modelled as exponential random processes. Each arc traversal is associated with an expected travel time and an expected cumulative hazard representing failure risk. Path reliability is interpreted probabilistically through a survival function, leading to a fractional objective that minimises expected travel time per unit of expected hazard. Based on this formulation, a new labelling-based algorithm is developed that incrementally constructs feasible paths, applies deterministic Pareto dominance on expectations rules to eliminate suboptimal partial routes and identifies the optimal solution without exhaustive enumeration. Computational experiments on randomly generated AGV networks demonstrate that the proposed algorithm consistently identifies the optimal fractional path whilst significantly reducing computational effort compared with complete Pareto enumeration and other benchmark approaches. Sensitivity analyses further illustrate the algorithm's robustness to variations in failure rates and network density. The results confirm that the proposed stochastic formulation provides a physically consistent and computationally efficient framework for reliability-aware AGV routing in manufacturing environments.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-Driven Manufacturing Decision Optimisation: An Empirical Study Based on Human–Machine Collaboration 基于人机协作的人工智能驱动制造决策优化实证研究

IF 3.3

IET Collaborative Intelligent Manufacturing Pub Date : 2026-07-20 DOI: 10.1049/cim2.70068

Jinhui Wang, Yangli Lei

{"title":"AI-Driven Manufacturing Decision Optimisation: An Empirical Study Based on Human–Machine Collaboration","authors":"Jinhui Wang, Yangli Lei","doi":"10.1049/cim2.70068","DOIUrl":"https://doi.org/10.1049/cim2.70068","url":null,"abstract":"With the context of the human-centric paradigm of Industry 5.0, this paper empirically tests the extent to which human–machine collaboration modes contribute to the optimisation of decision-making in manufacturing. It determines the impact of different types of collaboration on economic efficiency, resilience and sustainability and discusses the role of AI capabilities in this process and the moderating role played by the level of digital transformation of enterprises. A mixed methodology was used. A large-scale questionnaire survey (n = 519) of manufacturing managers across different industries and case-based interviews were adopted. The theoretical framework was examined through structural equation modelling (SEM) because hypotheses were formulated based on paradox theory, and direct, mediating and moderating effects were analysed. Findings affirm meaningful dimension-specific direct effects; task-division collaboration increases economic efficiency, real-time interaction increases resilience and decision-complementarity collaboration increases sustainability. In part, these relationships are mediated by AI abilities (efficiency in data processing, adaptive learning speed, friendliness of the interface). Although the moderating role of the stage of digital transformation is statistically not significant, the analysis of heterogeneity shows significant differences across industries. The paper has three contributions: it creates a rigorous, mutually exclusive set of typologies of three modes of collaboration to fill classification gaps; defines the criteria of selection of instrumental AI capabilities and their association with collaborative decision-making and enumerates the factory-level evaluation standards of decision optimisation. It provides a new understanding of the micro-mechanisms of representation of collaborative practices into multi-dimensional optimisations that would bridge the Industry 4.0 and 5.0 transformations.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial-Scheduling Strategy for Irregularly Shaped Blocks Under Disruption in Shipbuilding 船舶制造中不规则形状块体中断的空间调度策略

IF 3.3

IET Collaborative Intelligent Manufacturing Pub Date : 2026-07-11 DOI: 10.1049/cim2.70066

Ningfei Wang, Jingbo Yin, Rafi Ullah Khan

{"title":"Spatial-Scheduling Strategy for Irregularly Shaped Blocks Under Disruption in Shipbuilding","authors":"Ningfei Wang, Jingbo Yin, Rafi Ullah Khan","doi":"10.1049/cim2.70066","DOIUrl":"https://doi.org/10.1049/cim2.70066","url":null,"abstract":"In shipyard construction, blocks processed at workstations are transported to a planar storage yard (PSY), where disruptions may significantly affect scheduling efficiency and spatial arrangement. To address the practical problem of block layout and scheduling under disruption conditions, this study proposes a mixed-integer programming model with the objectives of maximising PSY utilisation and minimising vehicle scheduling time. To improve spatial utilisation, a geometric feature-based layout strategy incorporating supplementary-angle and supplementary-edge methods is developed for irregular block arrangement. In addition, a disruption-recovery scheduling strategy considering both workstation and vehicle disruptions is proposed to dynamically adjust block scheduling sequences. A two-stage artificial bee colony (ABC) algorithm is introduced to generate high-quality initial solutions. To efficiently solve the proposed problem, a hybrid collaborative optimisation framework integrating ABC, adaptive large neighbourhood search (ALNS) and a disruption-recovery algorithm is developed. The ALNS mechanism enhances local search capability through destroy and repair operators, whereas the disruption-recovery module dynamically identifies suitable recovery strategies under different disruption scenarios. Comparative experiments and sensitivity analyses based on real shipyard cases demonstrate that the proposed method improves space utilisation, reduces vehicle travel time and maintains strong robustness under dynamic disruption conditions.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unveiling the Impact of Artificial Intelligence on Automotive Supply Chain Operations 揭示人工智能对汽车供应链运营的影响

IF 3.3

IET Collaborative Intelligent Manufacturing Pub Date : 2026-06-21 DOI: 10.1049/cim2.70065

Hajar Fatorachian, Ashima Ashima

{"title":"Unveiling the Impact of Artificial Intelligence on Automotive Supply Chain Operations","authors":"Hajar Fatorachian, Ashima Ashima","doi":"10.1049/cim2.70065","DOIUrl":"https://doi.org/10.1049/cim2.70065","url":null,"abstract":"This study examines the transformative impact of artificial intelligence (AI) on supply chain management (SCM) within the automotive manufacturing sector, with a focus on AI adoption, its influence on key performance indicators (KPIs) and integration challenges. A mixed-methods research design was employed, combining quantitative survey data collected from supply chain professionals with qualitative insights derived from semi-structured interviews with industry experts. The quantitative data were analysed using statistical techniques to assess the relationship between AI adoption and SCM performance, whereas thematic analysis of interview data provided deeper contextual understanding of organisational and technological dynamics. The results indicate that AI adoption significantly improves operational performance, particularly in inventory turnover, delivery lead times and order accuracy. However, the findings also reveal critical barriers, including data quality limitations, system integration complexities, ethical concerns related to algorithmic bias and data privacy and potential workforce displacement. The study further identifies organisational readiness—driven by leadership commitment, innovation-oriented culture and robust technological infrastructure—as a key enabler of successful AI implementation. Based on these findings, the study recommends the development of structured AI adoption frameworks, including phased implementation strategies, investment in digital infrastructure, and targeted workforce reskilling initiatives. It also emphasises the need for ethical governance mechanisms to ensure transparency, accountability, and responsible AI deployment. These insights provide actionable guidance for practitioners and policymakers seeking to enhance supply chain performance while addressing the broader socio-technical implications of AI integration.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Industrial Robotics From Industry 4.0 to Industry 5.0: An Analysis of Architectures, Human-Centricity and Sustainability 工业机器人从工业4.0到工业5.0：架构、以人为中心和可持续性分析

IF 3.3

IET Collaborative Intelligent Manufacturing Pub Date : 2026-06-21 DOI: 10.1049/cim2.70064

Md. Abdus Shabur

{"title":"Industrial Robotics From Industry 4.0 to Industry 5.0: An Analysis of Architectures, Human-Centricity and Sustainability","authors":"Md. Abdus Shabur","doi":"10.1049/cim2.70064","DOIUrl":"https://doi.org/10.1049/cim2.70064","url":null,"abstract":"The evolution of industrial robotics is increasingly shaped by the transition from Industry 4.0 to Industry 5.0, where manufacturing extends beyond automation efficiency toward intelligent, human-centric, sustainable and resilient systems. This study presents a systematic literature review of 137 peer-reviewed articles, conducted following PRISMA guidelines, to examine the changing role of industrial robotics across technological, human and value-oriented dimensions. The findings show that industrial robots have evolved from isolated automation units into cyber-physical, data-driven and intelligent components embedded within smart manufacturing architectures. Key enabling technologies, including digital twins, artificial intelligence, IIoT and cloud edge computing, are identified as primary drivers of this transformation. Beyond technological advancements, the review highlights a shift toward human-centric manufacturing, where collaborative robotics, ergonomic design, worker augmentation and explainable AI redefine human–machine interaction. The results further indicate that Industry 5.0 introduces new evaluation criteria, emphasising lifecycle sustainability, energy efficiency, circular manufacturing and system resilience. However, challenges remain, including interoperability limitations, high implementation costs, organisational resistance and governance gaps in AI-enabled systems. This review develops an integrated framework linking architectures, applications, human factors and Industry 5.0 values and outlines key research directions for advancing responsible and sustainable industrial robotics.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Industrial Metaverse Platform for Collaborative and Sustainable Manufacturing in Industry 5.0 工业5.0中协同和可持续制造的工业元空间平台

IF 3.3

IET Collaborative Intelligent Manufacturing Pub Date : 2026-06-20 DOI: 10.1049/cim2.70062

Antonella Petrillo, Aniello Ferraro, Antonio Garofalo, Fabio De Felice

{"title":"An Industrial Metaverse Platform for Collaborative and Sustainable Manufacturing in Industry 5.0","authors":"Antonella Petrillo, Aniello Ferraro, Antonio Garofalo, Fabio De Felice","doi":"10.1049/cim2.70062","DOIUrl":"https://doi.org/10.1049/cim2.70062","url":null,"abstract":"The transition towards Industry 5.0 demands manufacturing systems that seamlessly integrate digital technologies with human-centric and sustainability objectives, enabling resilient and data-driven operations. This paper introduces an integrated industrial metaverse platform that unifies real-time Internet of things (IoT) sensing, dynamic digital twin modelling and immersive collaborative environments to support multistakeholder interaction, operational awareness and sustainability monitoring in manufacturing systems. The platform enables users to interact with a realistic 3D virtual representation of the factory via desktop and extended reality (XR) interfaces, accessing contextualised data on machine performance, energy use, environmental conditions and emissions. By embedding sustainability key performance indicators—such as energy consumption, CO2 emissions and waste metrics—directly within the immersive environment, the platform moves beyond conceptual frameworks and offers an operational solution that supports predictive maintenance, process optimisation and collaborative decision-making aligned with human-centric manufacturing goals. A pilot implementation in a textile manufacturing setting demonstrates improvements in operational transparency, reduced downtime, enhanced remote collaboration and greater energy efficiency. Results illustrate the potential of industrial metaverse technologies to advance sustainable, resilient and human-centric manufacturing, providing a scalable framework suitable for broader industrial adoption.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Privacy-Preserving Approach for Collaborative Optimization Across Company Borders 跨公司边界协作优化的隐私保护方法

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-06-10 DOI: 10.1049/cim2.70063

Du Nguyen Duy, Bernhard Fischer, Vittorio Solina, Michael Affenzeller

{"title":"A Privacy-Preserving Approach for Collaborative Optimization Across Company Borders","authors":"Du Nguyen Duy, Bernhard Fischer, Vittorio Solina, Michael Affenzeller","doi":"10.1049/cim2.70063","DOIUrl":"https://doi.org/10.1049/cim2.70063","url":null,"abstract":"In modern manufacturing value chains, achieving innovation and optimal performance often requires strong collaboration across organizational boundaries. Tackling the multi-party optimization problems arising from these partnerships necessitates methodologies that can learn from complementary datasets, where each partner provides unique partial features for a shared product or process. Although Data-Driven Evolutionary Optimization (DDEO) is an effective paradigm for solving complex optimization problems, its potential is limited by the need to centralise sensitive proprietary data. Although initial research on privacy-preserving DDEO has emerged, these efforts focus exclusively on horizontally partitioned data and rely on weight aggregation methods that are structurally incompatible with vertically partitioned data. Consequently, secure collaborative optimization across value chains—where data are vertically distributed among partners with distinct features—remains largely unaddressed. To bridge this gap, we propose a novel protocol enabling secure DDEO on feature-partitioned datasets. The architecture protects information in transit via standard encryption and uses Trusted Execution Environments (TEEs) to securely aggregate and process the combined datasets within a hardware-isolated enclave. This creates a versatile, model-agnostic computational environment that accommodates diverse methodologies, allowing practitioners to leverage different algorithmic advantages. Empirical evaluations using four state-of-the-art DDEO algorithms show that the proposed protocol achieves solution quality statistically identical to an insecure centralised baseline. Ultimately, the framework provides a practical solution for multi-party optimization without compromising data confidentiality.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"8 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2026-04-30

引用次数: 0