IET Collaborative Intelligent Manufacturing最新文献_第5页

Toward a Human-Centric and Cognitive Integration Paradigm in Industry 5.0: Implications for Production Engineering 工业5.0中以人为中心的认知整合范式：对生产工程的启示

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-12-06 DOI: 10.1049/cim2.70050

Marcos Vido, Geraldo Neto, Francesco Facchini, Salvatore Digiesi

{"title":"Toward a Human-Centric and Cognitive Integration Paradigm in Industry 5.0: Implications for Production Engineering","authors":"Marcos Vido, Geraldo Neto, Francesco Facchini, Salvatore Digiesi","doi":"10.1049/cim2.70050","DOIUrl":"10.1049/cim2.70050","url":null,"abstract":"The fifth industrial revolution, known as Industry 5.0 (I5.0), has a vision for a new, resilient, socio-centred and competitive industry. The new approach provides a vision to enhance human-machine interaction (HMI) and assist operators efficiently. This study investigates the integration of human-centric principles within Industry 5.0, specifically in production engineering, with a central focus on the collaboration between humans and machines. Through an extensive literature review, the research identifies emerging trends and significant gaps in the current body of knowledge, especially regarding the development of intuitive and flexible interfaces, ethical frameworks in automated systems, and the management of cognitive load within manufacturing environments. The findings reveal considerable gaps in understanding the practical application of HMI across various industrial settings, emphasising the need for production technologies that enhance capabilities and advance a sustainable, ethical, and human-centred manufacturing landscape. This research contributes to the ongoing discourse on I5.0 by advocating for frameworks that prioritise human-centric values alongside technological innovation.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145686290","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}

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IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-12-06

引用次数: 0

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-12-04

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Cover 封面

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-12-04 DOI: 10.1049/cim2.70030

Nanxing Chen, Yong Chen, Wenchao Yi, Zhi Pei

引用次数: 0

Inter-Turn Fault Diagnosis of Induction Motor Using a Root-Prony and Fuzzy Logic Method 基于根算子和模糊逻辑的异步电动机转间故障诊断

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-11-10 DOI: 10.1049/cim2.70048

Mohamed Kouadria, Ahmed Zakaria Mehdi Chedjara, Hafiz Ahmed, Chun-Lien Su, Mohamed Benbouzid, Josep M. Guerrero

{"title":"Inter-Turn Fault Diagnosis of Induction Motor Using a Root-Prony and Fuzzy Logic Method","authors":"Mohamed Kouadria, Ahmed Zakaria Mehdi Chedjara, Hafiz Ahmed, Chun-Lien Su, Mohamed Benbouzid, Josep M. Guerrero","doi":"10.1049/cim2.70048","DOIUrl":"https://doi.org/10.1049/cim2.70048","url":null,"abstract":"This paper proposes a novel diagnostic approach for detecting inter-turn short-circuit faults in induction motors, combining the root-Prony method with fuzzy logic. Traditional techniques, such as the periodogram, have limitations in detecting low-magnitude harmonics and providing high-frequency resolution. To address these challenges, complex high-resolution methods, such as MUSIC and ESPRIT, have been developed. In this study, the root-Prony method is selected for its adaptability and low computational burden as it does not rely on space decomposition, making it faster than MUSIC. The proposed approach focuses on analysing the stator current signal within a specific frequency range near the fundamental rotor slot harmonics. By reducing the number of processed samples, computation time is further decreased. The integration of fuzzy logic enables intelligent decision-making regarding the condition of the stator circuit by considering harmonic magnitudes under different load torque values for accurate diagnosis. Experimental tests were conducted on an induction motor initially powered directly from an electrical network supplying symmetrical sinusoidal three-phase voltages. To demonstrate the robustness of the proposed method in noisy environments, additional tests were performed with the motor powered by a converter. In such scenarios, the conventional periodogram-based technique was unable to detect the desired harmonics due to the high harmonic content in the stator current signals. The test results confirm the superior effectiveness of the root-Prony method over the classical periodogram technique in estimating the frequencies and amplitudes of the targeted harmonics. The integration of the root-Prony method with fuzzy logic offers an advanced, efficient and reliable solution for fault diagnosis in induction motors.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521616","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}

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An Adaptive Machine Learning Framework Integrating AutoML and MLOps for Two-Stage Classification in Hard Disk Drive Manufacturing 集成自动学习和MLOps的硬盘制造两阶段分类自适应机器学习框架

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-10-22 DOI: 10.1049/cim2.70047

Natthakritta Rungtalay, Somyot Kaitwanidvilai

{"title":"An Adaptive Machine Learning Framework Integrating AutoML and MLOps for Two-Stage Classification in Hard Disk Drive Manufacturing","authors":"Natthakritta Rungtalay, Somyot Kaitwanidvilai","doi":"10.1049/cim2.70047","DOIUrl":"https://doi.org/10.1049/cim2.70047","url":null,"abstract":"This study aims to predict hard disk drives (HDDs) that pass initial testing but fail during reliability testing, using historical data from 8968 records with 218 features, such as head position and flying height of the read/write head. Since reliability testing is time-intensive, early failure prediction can significantly accelerate problem detection and resolution. The research focuses on detecting fly height modulation, a key symptom of HDD failure, and introduces an adaptive machine learning (ML) framework integrating AutoML for optimised model selection and hyperparameter tuning with MLOps for deployment, monitoring and continuous updates. Building on a previously proposed dual-stage classification framework that combines novelty detection and supervised learning, the proposed framework addresses the inefficiencies of manual hyperparameter tuning inherent in the earlier methods. The proposed framework achieves 92% accuracy in novelty detection and 100% in supervised learning, outperforming prior approaches. This integration of AutoML and MLOps offers a scalable, robust solution for early failure prediction, enabling real-time adaptability with minimal human intervention. Future work will focus on enhancing computational efficiency and responsiveness to data shifts and drifts, advancing data-driven decision-making in reliability testing.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145366497","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

Revolutionising Vaccine Supply Chains: A Federated Blockchain Framework With Smart Contracts for Enhanced Scalability and Security 革命性的疫苗供应链：具有增强可扩展性和安全性的智能合约的联邦区块链框架

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-09-25 DOI: 10.1049/cim2.70040

Nirupam Saha, Rajesh Bose, Sandip Roy, Arfat Ahmad Khan, Shrabani Sutradhar, Somnath Mondal, Mohd Asif Shah

{"title":"Revolutionising Vaccine Supply Chains: A Federated Blockchain Framework With Smart Contracts for Enhanced Scalability and Security","authors":"Nirupam Saha, Rajesh Bose, Sandip Roy, Arfat Ahmad Khan, Shrabani Sutradhar, Somnath Mondal, Mohd Asif Shah","doi":"10.1049/cim2.70040","DOIUrl":"10.1049/cim2.70040","url":null,"abstract":"Efficiency, security and transparency are critical for public health issues such as counterfeit vaccines, cold chain integrity and data authenticity. This study introduces a decentralised smart contract-driven blockchain framework that incorporates federated learning and edge computing to overcome the limitations of traditional centralised systems. Through decentralised decision-making and real-time data management, the proposed framework facilitates traceability, reduces vaccine wastage and ensures robust cold chain monitoring. The framework applied hybrid evaluation using Blockchain Impact Modelling (BIM) and Multi-Criteria Decision Analysis (MCDA) in order to show prominent improvement in scalability, security and operational efficiency. The experimental results confirm that the framework can detect temperature anomalies 76% faster, increase transaction throughput by 36.1% and improve breach detection to 99.7%. Smart contracts enable automated and accurate decision-making, whereas FL ensures privacy-preserving analytics among stakeholders. EC integration reduces latency and computational overhead, allowing real-time responses. This new architecture provides a strong foundation for vaccine supply chain management, addressing global challenges while adapting to different regulatory landscapes. This framework marks a new benchmark in securing healthcare logistics and extending its applicability to broader pharmaceutical supply chains.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146547","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

Improved Multi-Objective Evolution Algorithm for Energy-Aware Distributed Heterogeneous Hybrid Flowshop Scheduling Problem 能量感知分布式异构混合流水车间调度问题的改进多目标进化算法

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-09-12 DOI: 10.1049/cim2.70046

Yingli Li, Haibing Liu, Biao Zhang

{"title":"Improved Multi-Objective Evolution Algorithm for Energy-Aware Distributed Heterogeneous Hybrid Flowshop Scheduling Problem","authors":"Yingli Li, Haibing Liu, Biao Zhang","doi":"10.1049/cim2.70046","DOIUrl":"10.1049/cim2.70046","url":null,"abstract":"This study investigates the distributed heterogeneous hybrid flow-shop scheduling problem (DHHFSP) with the tardiness and energy consumption criteria. A decomposition-based multi-objective artificial bee colony (MOABC/D) algorithm is developed to solve the scheduling problem. In the MOABC/D algorithm, a tri-level encoding scheme combined with domain-specific heuristic rules are designed to enable comprehensive solution space exploration. A local search framework incorporating five novel critical path-based neighbourhood structures to intensify subproblem investigation. An adaptive optimisation strategy integrating similarity-based prioritisation, dynamic neighbourhood relationships, and coordinated information sharing among adjacent subproblems. A solution exchange strategy is proposed to assist the algorithm jump out of the local optimum, and continue searching for solutions in various directions. Comprehensive simulation trials validate the algorithm's ability to balance scheduling efficiency and energy conservation in the DHHFSP. It shows great promise for multi-objective optimisation in complex distributed manufacturing systems with heterogeneous resources.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145038120","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 Parametric FEA Model for Evaluation of Structural Elements Subjected to Varying Loading Conditions 结构单元在变载荷条件下的参数化有限元分析模型

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-09-11 DOI: 10.1049/cim2.70045

Xiaoqin Wang, Zhuming Bi

{"title":"A Parametric FEA Model for Evaluation of Structural Elements Subjected to Varying Loading Conditions","authors":"Xiaoqin Wang, Zhuming Bi","doi":"10.1049/cim2.70045","DOIUrl":"10.1049/cim2.70045","url":null,"abstract":"The value of a product relies greatly on the significance of the innovations when a product is designed and manufactured. Whereas numerous computer aided design (CAD) tools, such as computer aided engineering (CAE) and artificial intelligence (AI) tools, are widely used to create and accelerate the innovations in engineering design, commercially available AI tools sacrifice the efficiency for generality in virtual design. Because the behaviours of a physical model must be interpreted as governing mathematical models, this may ignore key analytical correspondence of inputs and outputs in the physical model. Design optimisation is simulation based with a limited exploration of a design space. We argue that for innovations such as routine designs or parametric designs that are rooted in knowledge-based engineering (KBE), a sophisticated tool, rather than a general-purpose CAE tool, should be developed to optimise a design solution analytically. To illustrate the feasibility and effectiveness of the proposed idea, a parametric FEA model was developed for a client company in construction. The model is programed and implemented, its conciseness, efficiency and accuracy was proven by comparative studies with SolidWorks simulation. It was recommended and used by the client company for practical use.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145037723","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

DLC-NddMode: A Spiking Neural Network Tactile Object Recognition Model With Adaptive Optimisation and Regularisation DLC-NddMode：一种自适应优化和正则化的脉冲神经网络触觉物体识别模型

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-09-06 DOI: 10.1049/cim2.70043

Lin Liu, Shaobo Li, Xiaoyang Ji, Jing Yang, Zukun Yu

{"title":"DLC-NddMode: A Spiking Neural Network Tactile Object Recognition Model With Adaptive Optimisation and Regularisation","authors":"Lin Liu, Shaobo Li, Xiaoyang Ji, Jing Yang, Zukun Yu","doi":"10.1049/cim2.70043","DOIUrl":"10.1049/cim2.70043","url":null,"abstract":"Empowering robots with tactile perception is crucial for the future development of intelligent robots. Tactile perception can expand the application scenarios of robots to perform more complex tasks. Unfortunately, existing approaches are flawed in their use of data collected by robotic tactile sensors because they either do not consider that tactile sensation is event-driven, which means that tactile data are spatiotemporal, or they ignore that too few samples of tactile data would cause overfitting problems in the network model. We introduce DLC-NddModel, a method based on spiking neural networks (SNNs) that incorporates Adam optimisation, regularisation and cosine annealing method. DLC-NddModel aims to fully interpret the spatiotemporal nature of the tactile data using the spatiotemporal dynamics of SNNs and to alleviate the overfitting problem caused by the few samples. Furthermore, unlike previous work using SNNs, we use a different approximation function to surmount the nondifferentiable spiking activity of the spiking neurons, thus making the gradient descent method usable and effective. To effectively alleviate the overfitting problem caused by too few tactile data samples, we explore solutions through regularisation strategies that add training noise or regularisation terms to the loss function. We compare DLC-NddModel against four prior state-of-the-art approaches on the EvTouch-Objects tactile spike dataset. Our experimental results demonstrate that DLC-NddModel has higher recognition accuracy than the comparison method when recognising household object data with an ACC value improvement of at least 2.362%.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145005601","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