IET Collaborative Intelligent Manufacturing最新文献_第7页

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-05-19

引用次数: 0

Correction to “Comprehensive Systematic Literature Review on Cognitive Workload: Trends on Methods, Technologies, and Case Studies” 对“认知负荷的综合系统文献综述：方法、技术和案例研究的趋势”的更正

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-05-19 DOI: 10.1049/cim2.70034

引用次数: 0

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-05-09

引用次数: 0

Bridging the gap: Empowering manufacturing and production small medium enterprises through industrial Internet of Things adoption model 弥合差距：通过工业物联网采用模式，为制造业和生产型中小企业赋权

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-05-09 DOI: 10.1049/cim2.70021

Sajid Shah, Syed Hamid Hussain Madni, Siti Zaitoon Mohd Hashim, Muhammad Faheem, Hafiz Muhammad Faisal Shahzad

{"title":"Bridging the gap: Empowering manufacturing and production small medium enterprises through industrial Internet of Things adoption model","authors":"Sajid Shah, Syed Hamid Hussain Madni, Siti Zaitoon Mohd Hashim, Muhammad Faheem, Hafiz Muhammad Faisal Shahzad","doi":"10.1049/cim2.70021","DOIUrl":"10.1049/cim2.70021","url":null,"abstract":"The industrial Internet of Things (IIoT) is revolutionising manufacturing and production of small and medium enterprises (SMEs) by enhancing efficiency and product quality. While developed countries like the USA, UK, Canada, Finland, and Japan have widely adopted IIoT, developing nations such as Bangladesh, India, Pakistan, and Malaysia are still lagging. This study explores IIoT adoption in manufacturing SMEs, emphasising its potential for economic growth despite challenges like budget constraints and skill gaps in developing countries. It presents a novel model based on 17 factors from the TOEI (Technology, Organization, Environment, and Individual) framework to support decision-makers in integrating IIoT technologies. The model’s reliability and validity are confirmed through rigorous testing and a survey of three SMEs. This proposed model serves as a roadmap for SMEs, breaking down complex processes into manageable steps, and providing SMEs with a structured approach.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143930344","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 Adaptive Whole-Body Control Approach for Dynamic Obstacle Avoidance of Mobile Manipulators for Human-Centric Smart Manufacturing 以人为中心的智能制造中移动机械臂动态避障的自适应全身控制方法

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-05-07 DOI: 10.1049/cim2.70031

Yong Tao, He Gao, Donghua Tan, Jiahao Wan, Baicun Wang, Chengxi Li, Pai Zheng

{"title":"An Adaptive Whole-Body Control Approach for Dynamic Obstacle Avoidance of Mobile Manipulators for Human-Centric Smart Manufacturing","authors":"Yong Tao, He Gao, Donghua Tan, Jiahao Wan, Baicun Wang, Chengxi Li, Pai Zheng","doi":"10.1049/cim2.70031","DOIUrl":"10.1049/cim2.70031","url":null,"abstract":"In human-centric smart manufacturing (HCSM), the robot's dynamic obstacle avoidance function is crucial to ensuring human safety. Unlike the static obstacle avoidance of manipulators or mobile robots, the dynamic obstacle avoidance in mobile manipulators presents challenges such as high-dimensional planning and motion deadlock. In this paper, an adaptive whole-body control approach for dynamic obstacle avoidance of the mobile manipulators for HCSM is proposed. Firstly, an adaptive global path planning method is proposed to reduce planning dimension. Secondly, lateral coupling effect term and nonlinear velocity damping constraints are formulated to alleviate motion deadlock. Then, a whole-body dynamic obstacle avoidance motion controller is presented. Through simulations and real-world experiments, the planning time is reduced by 18.65% on average, and the path length by 15.94%, compared to the global RRT benchmark algorithm. The dynamic obstacle avoidance experiment simulates the obstacle combinations such as pedestrians moving in opposite direction, traversing and forming a circle during the robot operation. The proposed motion controller can adjust robot movement in real time according to the change of its relative distance from obstacles, meanwhile maintaining an average safe distance of 0.45 m from dynamic obstacles. It is assumed that the proposed approach can benefit dynamic human–robot symbiotic manufacturing tasks from more natural and efficient manipulations.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919884","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 : 2025-05-07

引用次数: 0

Artificial Intelligence for Pharmaceutical Quality Assurance in Kenya 人工智能在肯尼亚的药品质量保证

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-05-06 DOI: 10.1049/cim2.70033

Samuel Inshutiyimana, Kush Rajeshbhai Rana, Fatuma Ali Abdullahi, Michael Matiop Aleu

{"title":"Artificial Intelligence for Pharmaceutical Quality Assurance in Kenya","authors":"Samuel Inshutiyimana, Kush Rajeshbhai Rana, Fatuma Ali Abdullahi, Michael Matiop Aleu","doi":"10.1049/cim2.70033","DOIUrl":"10.1049/cim2.70033","url":null,"abstract":"Artificial intelligence is transforming the pharmaceutical sector through improvement in critical processes such as quality assurance (QA). However, in Kenya, technical problems in QA processes, including in-process quality control, equipment maintenance, and visual inspections exist. This paper aims to shed light on the potential of AI in improving pharmaceutical QA in Kenya and challenges associated with its integration. A literature search was thoroughly conducted by retrieving articles from Google Scholar. Articles and policy documents with information relevant to AI applications in QA, optimising pharmaceutical processes, and regulatory compliance in Kenya were reviewed and analysed. AI can improve efficiency and precision in various QA processes including warehousing, equipment maintenance, in-process quality control, and visual inspections, among others. Significant challenges to AI incorporation in QA of Kenya's pharma companies include a lack of technical expertise and understanding of AI outcomes, high implementation costs and fear of losing jobs. There should be strengthened collaborations among government, pharmaceutical manufacturers, AI companies, and researchers to address skill-based barriers and financial challenges.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914510","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 : 2025-05-06

引用次数: 0

A Digital Twin and Big Data-Driven Opti-State Control Framework for Production Logistics Synchronisation System 生产物流同步系统的数字孪生和大数据驱动的最优状态控制框架

IF 3.1

IET Collaborative Intelligent Manufacturing Pub Date : 2025-04-29 DOI: 10.1049/cim2.70024

Yongheng Zhang, Zhicong Hong, Yafeng Wei, Ting Qu, Geroge Q. Huang

{"title":"A Digital Twin and Big Data-Driven Opti-State Control Framework for Production Logistics Synchronisation System","authors":"Yongheng Zhang, Zhicong Hong, Yafeng Wei, Ting Qu, Geroge Q. Huang","doi":"10.1049/cim2.70024","DOIUrl":"10.1049/cim2.70024","url":null,"abstract":"The randomness and persistence of dynamic disturbances pose significant challenges to resource integration, task allocation, and goal setting within production logistics system. To maintain the optimal operational state of production logistics system over the long term, predictive planning and intervention must occur before disturbances arise, whereas adaptive adjustments are necessary to correct system states after disturbances occur. However, the effective implementation of these control strategies is hindered by several obstacles, such as a lack of comprehensive data and valuable knowledge, which impedes the support for opti-state control (OsC). Fortunately, with the advancements in information technologies such as the IoT and digital twins, it is now possible to collect and process vast amounts of real-time, full-lifecycle big data, thereby enabling more informed optimisation decisions. This paper proposes a digital twin and big data-based opti-state control system (DTBD-OsCS). The architecture integrates big data analytics and service-driven patterns, effectively addressing the aforementioned challenges. Within this framework, both predictive opti-state control (POsC) and adaptive opti-state control (AOsC) strategies are incorporated, along with the development of key technologies for implementing big data analysis. The proposed architecture's effectiveness is demonstrated through application scenarios, and experimental results and findings are thoroughly discussed. The results show that the proposed architecture significantly enhances the efficiency of production logistics systems and effectively reduces the cost impact of disturbances on the system.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"7 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889045","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 : 2025-04-29

引用次数: 0