IET Collaborative Intelligent Manufacturing最新文献_第7页

Shop floor dispatching with variable urgent operations based on Workload Control: An assessment by simulation 基于工作量控制的变紧急作业车间调度：一种仿真评估

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2023-09-21 DOI: 10.1049/cim2.12084

Mingze Yuan, Lin Ma, Ting Qu, Matthias Thürer

{"title":"Shop floor dispatching with variable urgent operations based on Workload Control: An assessment by simulation","authors":"Mingze Yuan, Lin Ma, Ting Qu, Matthias Thürer","doi":"10.1049/cim2.12084","DOIUrl":"https://doi.org/10.1049/cim2.12084","url":null,"abstract":"Meeting customer time requirements poses a major challenge in the context of high-variety make-to-order companies. Companies need to reduce the lead time and process urgent jobs in time, while realising high delivery reliability. The key decision stages within Workload Control (WLC) are order release and shop floor dispatching. To the best of our knowledge, recent research has mainly focused on order release stage and inadvertently ignored shop floor dispatching stage. Meanwhile, urgency of job is not only related to its due date, but also affected by the dynamics of shop floor. Specifically, urgency of jobs may decrease at downstream operations in the job's routing, since priority dispatching for urgent jobs accelerates production speed at the upstream operations. And occupying production resources increases the waiting time of non-urgent jobs at workstation. This phenomenon leads to the change of urgency of jobs. Misjudgement of urgent jobs therefore may result in actual urgent jobs not being processed in time. In response, the authors focus on shop floor dispatching stage and consider the transient status of urgent operations in the context of WLC. The urgency of jobs is rejudged at the input buffer of each workstation, which is firstly defined as urgent operations and non-urgent operations. Using simulation, the results show that considering the transient status of urgent operations contributes to speeding up production for actual urgent jobs and meeting delivery performance both in General Flow Shop and Pure Job Shop. In addition, percentage tardy performance is greatly affected by norm levels, especially at the severe urgent level. These have important implications on how urgent operations should be designed and how norm level should be set at shop floor dispatching stage.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"5 3","pages":""},"PeriodicalIF":8.2,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50140597","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

5G supporting digital servitization in manufacturing: An exploratory survey 5G支持制造业数字化服务：一项探索性调查

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2023-09-11 DOI: 10.1049/cim2.12083

Chiara Cimini, Alexandra Lagorio, Roberto Pinto, Giuditta Pezzotta, Federico Adrodegari, Sergio Cavalieri

{"title":"5G supporting digital servitization in manufacturing: An exploratory survey","authors":"Chiara Cimini, Alexandra Lagorio, Roberto Pinto, Giuditta Pezzotta, Federico Adrodegari, Sergio Cavalieri","doi":"10.1049/cim2.12083","DOIUrl":"https://doi.org/10.1049/cim2.12083","url":null,"abstract":"Digital servitization is a business model transformation process enabled by the use of digital technologies to create or improve industrial services and product-service offerings by creating value and competitive advantage increasing customer satisfaction and loyalty as well as company revenue streams. 5G networks can enable digital servitization of manufacturing by providing faster, more secure, and more reliable communications between machines, devices, and humans. This paper explores the impact of adopting 5G technologies on servitization and identifies the services that can benefit most from 5G networks. The research consists of two parts: a literature review of the technologies currently used in the design and provision of industrial services that could benefit from 5G networks and an exploratory survey involving manufacturing companies that have started the digital servitization journey. The main results emerging from the research suggest that 5G can profoundly impact services supported by Augmented Reality, Cloud computing, and Cyber-physical systems, mainly concerning maintenance, workforce training, machine diagnosis and monitoring.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"5 3","pages":""},"PeriodicalIF":8.2,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50129369","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

Deep Q-learning recommender algorithm with update policy for a real steam turbine system 基于更新策略的汽轮机系统深度Q学习推荐算法

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2023-09-02 DOI: 10.1049/cim2.12081

Mohammad Hossein Modirrousta, Mahdi Aliyari Shoorehdeli, Mostafa Yari, Arash Ghahremani

{"title":"Deep Q-learning recommender algorithm with update policy for a real steam turbine system","authors":"Mohammad Hossein Modirrousta, Mahdi Aliyari Shoorehdeli, Mostafa Yari, Arash Ghahremani","doi":"10.1049/cim2.12081","DOIUrl":"10.1049/cim2.12081","url":null,"abstract":"In modern industrial systems, diagnosing faults in time and using the best methods becomes increasingly crucial. It is possible to fail a system or to waste resources if faults are not detected or are detected late. Machine learning and deep learning (DL) have proposed various methods for data-based fault diagnosis, and the authors are looking for the most reliable and practical ones. A framework based on DL and reinforcement learning (RL) is developed for fault detection. The authors have utilised two algorithms in their work: Q-Learning and Soft Q-Learning. Reinforcement learning frameworks frequently include efficient algorithms for policy updates, including Q-learning. These algorithms optimise the policy based on the predictions and rewards, resulting in more efficient updates and quicker convergence. The authors can increase accuracy, overcome data imbalance, and better predict future defects by updating the RL policy when new data is received. By applying their method, an increase of 3%–4% in all evaluation metrics by updating policy, an improvement in prediction speed, and an increase of 3%–6% in all evaluation metrics compared to a typical backpropagation multi-layer neural network prediction with comparable parameters is observed. In addition, the Soft Q-learning algorithm yields better outcomes compared to Q-learning.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"5 3","pages":""},"PeriodicalIF":8.2,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42798281","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 overview on bipedal gait control methods 两足步态控制方法综述

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2023-09-01 DOI: 10.1049/cim2.12080

Chenghao Hu, Sicheng Xie, Liang Gao, Shengyu Lu, Jingyuan Li

引用次数: 0

A metaworld: Implications, opportunities and risks of the metaverse 元世界:元世界的影响、机遇和风险

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2023-08-12 DOI: 10.1049/cim2.12079

Fabio De Felice, Mizna Rehman, Antonella Petrillo, Ilaria Baffo

引用次数: 3

Industrial-generative pre-trained transformer for intelligent manufacturing systems 用于智能制造系统的工业生成预训练变压器

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2023-06-28 DOI: 10.1049/cim2.12078

Han Wang, Min Liu, Weiming Shen

{"title":"Industrial-generative pre-trained transformer for intelligent manufacturing systems","authors":"Han Wang, Min Liu, Weiming Shen","doi":"10.1049/cim2.12078","DOIUrl":"10.1049/cim2.12078","url":null,"abstract":"Manufacturing enterprises are facing how to utilise industrial knowledge and continuously accumulating massive unlabelled data to achieve human-cyber-physical collaborative and autonomous intelligence. Recently, artificial intelligence-generative content has achieved great performance in several domains and scenarios. A new concept of industrial generative pre-trained Transformer (Industrial-GPT) for intelligent manufacturing systems is introduced to solve various scenario tasks. It refers to pre-training with industrial datasets, fine-tuning with industrial scenarios, and reinforcement learning with domain knowledge. To enable Industrial-GPT to better empower the manufacturing industry, Model as a Service is introduced to cloud computing as a new service mode, which provides a more efficient and flexible service approach by directly invoking the general model of the upper layer and customising it for specific businesses. Then, the operation mechanism of the Industrial-GPT driven intelligent manufacturing system is described. Finally, the challenges and prospects of applying the Industrial-GPT in the manufacturing industry are discussed.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"5 2","pages":""},"PeriodicalIF":8.2,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44839799","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: 1

A complexity assessment framework with structure entropy for a cloud-edge collaborative manufacturing system 基于结构熵的云边缘协同制造系统复杂性评估框架

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2023-04-17 DOI: 10.1049/cim2.12077

Jiajian Li, Yanjun Shi, Xueyan Sun, Dong Liu

引用次数: 0

Optimisation of collaborative supply transportation based on traffic road network topology 基于交通路网拓扑的协同供给运输优化

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2023-04-02 DOI: 10.1049/cim2.12076

Aihui Wang, Xiaobo Han, Wudai Liao, Ping Liu, Jingwen Song, Daming Li

{"title":"Optimisation of collaborative supply transportation based on traffic road network topology","authors":"Aihui Wang, Xiaobo Han, Wudai Liao, Ping Liu, Jingwen Song, Daming Li","doi":"10.1049/cim2.12076","DOIUrl":"10.1049/cim2.12076","url":null,"abstract":"With the rapid development of China's economy, enterprises need to plan their logistics transportation routes reasonably in advance. This will make the transportation service more efficient. For the supplier's transportation service problem, an analysis method of critical path nodes is provided and a multi-supplier collaborative transportation strategy is designed in this article. First, a model for minimising the transportation cost was established, then a path diagram was simulated and the optimal and alternative transportation paths of suppliers based on the k-shortest path algorithm were calculated. In addition, path node availability during COVID-19 is used as a research context in this article. A multi-stage path analysis method was provided by discussing different cases of critical path nodes, which can make a reasonable selection of paths in a timely and effective manner. Finally, simulations of collaborative transportation for suppliers were performed in three scenarios and the results verified the effectiveness of the collaborative transportation strategy. The proposed collaborative transportation strategy of suppliers not only strengthened the synergistic cooperation among suppliers, but also cultivated the potential customer for suppliers in this article. Furthermore, the strategy could improve the flexibility of the supply chain, maximise the overall efficiency and also provide a new solution for the development of logistics and transportation services.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"5 2","pages":""},"PeriodicalIF":8.2,"publicationDate":"2023-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45722056","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 review on learning to solve combinatorial optimisation problems in manufacturing 学习解决制造业中的组合优化问题综述

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2023-03-03 DOI: 10.1049/cim2.12072

Cong Zhang, Yaoxin Wu, Yining Ma, Wen Song, Zhang Le, Zhiguang Cao, Jie Zhang

{"title":"A review on learning to solve combinatorial optimisation problems in manufacturing","authors":"Cong Zhang, Yaoxin Wu, Yining Ma, Wen Song, Zhang Le, Zhiguang Cao, Jie Zhang","doi":"10.1049/cim2.12072","DOIUrl":"10.1049/cim2.12072","url":null,"abstract":"An efficient manufacturing system is key to maintaining a healthy economy today. With the rapid development of science and technology and the progress of human society, the modern manufacturing system is becoming increasingly complex, posing new challenges to both academia and industry. Ever since the beginning of industrialisation, leaps in manufacturing technology have always accompanied technological breakthroughs from other fields, for example, mechanics, physics, and computational science. Recently, machine learning (ML) technology, one of the crucial subjects of artificial intelligence, has made remarkable progress in many areas. This study thoroughly reviews how ML, specifically deep (reinforcement) learning, motivates new ideas for addressing challenging problems in manufacturing systems. We collect the literature targeting three aspects: scheduling, packing, and routing, which correspond to three pivotal cooperative production links of today's manufacturing system, that is, production, packing, and logistics respectively. For each aspect, we first present and discuss the state-of-the-art research. Then we summarise and analyse the development trends and point out future research opportunities and challenges.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"5 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48811465","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: 9

Robotic disassembly sequence planning considering parts failure features 考虑零件失效特征的机器人拆卸顺序规划

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2023-02-24 DOI: 10.1049/cim2.12074

Jia Cui, Can Yang, Jinliang Zhang, Sisi Tian, Jiayi Liu, Wenjun Xu

{"title":"Robotic disassembly sequence planning considering parts failure features","authors":"Jia Cui, Can Yang, Jinliang Zhang, Sisi Tian, Jiayi Liu, Wenjun Xu","doi":"10.1049/cim2.12074","DOIUrl":"10.1049/cim2.12074","url":null,"abstract":"Disassembly is an important step in remanufacturing products. Robotic disassembly helps to improve disassembly efficiency. However, the end-of-life products often have the parts with uncertain quality, which is manifested as wear, fracture, deformation, corrosion, and other failure features. The parts failure features always have impacts on disassembly process. First, the evaluation method of parts failure features is researched, and the quantitative model of parts failure features is constructed using fuzzy models. Then, the disassembly information model is established by considering the influence of different failure degrees on the robotic disassembly process. Afterwards, to generate the optimal disassembly solution, deep reinforcement learning (DRL) is used to solve robotic disassembly sequence planning problem which considers parts failure features. Considering the influence of parts failure features on robotic disassembly time, the states, actions and rewards and environment are designed in DRL. Finally, a case study of the double shaft coupling as a waste product is carried out, and the proposed method is compared with the other methods to verify the effectiveness.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"5 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41331718","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: 1