IET Collaborative Intelligent Manufacturing最新文献

{"title":"An approximate evaluation method for neighbourhood solutions in job shop scheduling problem","authors":"Lin Gui,&nbsp;Xinyu Li,&nbsp;Liang Gao,&nbsp;Jin Xie","doi":"10.1049/cim2.12049","DOIUrl":"10.1049/cim2.12049","url":null,"abstract":"<p>Job shop scheduling problem is a classical scheduling problem, and it is very difficult to work out. To solve it well, the meta-heuristic algorithm is a good choice, and the evaluation method of neighbourhood solutions, which affects the efficiency of the algorithm and the quality of the solution, is one of the keys in the algorithm. We propose an approximate evaluation method by exploring domain knowledge in neighbourhood solutions. Firstly, we reduce the computational time of the evaluation by analysing the unnecessary computational operations. Secondly, according to the domain knowledge, we prove that the evaluated value of the neighbourhood solution is the exact value under certain conditions. At the same time, a set of critical parameters are calculated to correct the estimated value of the neighbourhood solutions that do not meet the conditions to improve the evaluation accuracy. With all of these, an approximate evaluation method for neighbourhood solutions in job shop scheduling problems is proposed. The experiments on different numerical instances show the superiority of the method proposed.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"4 3","pages":"157-165"},"PeriodicalIF":8.2,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48275656","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}

{"title":"Privacy-preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis","authors":"Liqiao Xia,&nbsp;Pai Zheng,&nbsp;Jinjie Li,&nbsp;Wangchujun Tang,&nbsp;Xiangying Zhang","doi":"10.1049/cim2.12057","DOIUrl":"10.1049/cim2.12057","url":null,"abstract":"<p>Data-driven fault diagnosis approaches have been widely adopted due to their persuasive performance. However, data are always insufficient to develop effective fault diagnosis models in real manufacturing scenarios. Despite numerous approaches that have been offered to mitigate the negative effects of insufficient data, the most challenging issue lies in how to break down the data silos to enlarge data volume while preserving data privacy. To address this issue, a vertical federated learning (FL) model, privacy-preserving boosting tree, has been developed for collaborative fault diagnosis of industrial practitioners while maintaining anonymity. Only the model information will be shared under the homomorphic encryption protocol, safeguarding data privacy while retaining high accuracy. Besides, an Autoencoder model is provided to encourage practitioners to contribute and then improve model performance. Two bearing fault case studies are conducted to demonstrate the superiority of the proposed approach by comparing it with typical scenarios. This present study's findings offer industrial practitioners insights into investigating the vertical FL in fault diagnosis.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"4 3","pages":"208-219"},"PeriodicalIF":8.2,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45096955","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}

{"title":"A deep reinforcement learning based approach for dynamic distributed blocking flowshop scheduling with job insertions","authors":"Xueyan Sun,&nbsp;Birgit Vogel-Heuser,&nbsp;Fandi Bi,&nbsp;Weiming Shen","doi":"10.1049/cim2.12060","DOIUrl":"10.1049/cim2.12060","url":null,"abstract":"<p>The distributed blocking flowshop scheduling problem (DBFSP) with new job insertions is studied. Rescheduling all remaining jobs after a dynamic event like a new job insertion is unreasonable to an actual distributed blocking flowshop production process. A deep reinforcement learning (DRL) algorithm is proposed to optimise the job selection model, and local modifications are made on the basis of the original scheduling plan when new jobs arrive. The objective is to minimise the total completion time deviation of all products so that all jobs can be finished on time to reduce the cost of storage. First, according to the definitions of the dynamic DBFSP problem, a DRL framework based on multi-agent deep deterministic policy gradient (MADDPG) is proposed. In this framework, a full schedule is generated by the variable neighbourhood descent algorithm before a dynamic event occurs. Meanwhile, all newly added jobs are reordered before the agents make decisions to select the one that needs to be scheduled most urgently. This study defines the observations, actions and reward calculation methods and applies centralised training and distributed execution in MADDPG. Finally, a comprehensive computational experiment is carried out to compare the proposed method with the closely related and well-performing methods. The results indicate that the proposed method can solve the dynamic DBFSP effectively and efficiently.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"4 3","pages":"166-180"},"PeriodicalIF":8.2,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49055467","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}

{"title":"Intelligent fault diagnosis of rotating machinery using lightweight network with modified tree-structured parzen estimators","authors":"Jingkang Liang,&nbsp;Yixiao Liao,&nbsp;Zhuyun Chen,&nbsp;Huibin Lin,&nbsp;Gang Jin,&nbsp;Konstantinos Gryllias,&nbsp;Weihua Li","doi":"10.1049/cim2.12055","DOIUrl":"10.1049/cim2.12055","url":null,"abstract":"<p>Deep learning-based methods have been widely used in the field of rotating machinery fault diagnosis. It is of practical significance to improve the calculation speed of the model on the premise of ensuring accuracy, so as to realise real-time fault diagnosis. However, designing an efficient and lightweight fault diagnosis network requires expert knowledge to determine the network structure and adjust the hyperparameters of the network, which is time-consuming and laborious. In order to design fault diagnosis networks considering both time and accuracy effortlessly, a novel lightweight network with modified tree-structured parzen estimators (LN-MT) is proposed for intelligent fault diagnosis of rotating machinery. Firstly, a lightweight framework based on global average pooling and group convolution is proposed, and a hyperparameter optimisation (HPO) method based on Bayesian optimisation called tree-structured parzen estimator is utilised to automatically search the optimal hyperparameters for the fault diagnosis task. The objective of the HPO algorithm is the weighting of accuracy and calculating time, so as to find models that balance both time and accuracy. The results of comparison experiments indicate that LN-MT can achieve superior fault diagnosis accuracies with few trainable parameters and less calculating time.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"4 3","pages":"194-207"},"PeriodicalIF":8.2,"publicationDate":"2022-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44181517","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}

{"title":"Deep reinforcement learning-based balancing and sequencing approach for mixed model assembly lines","authors":"Youlong Lv,&nbsp;Yuanliang Tan,&nbsp;Ray Zhong,&nbsp;Peng Zhang,&nbsp;Junliang Wang,&nbsp;Jie Zhang","doi":"10.1049/cim2.12061","DOIUrl":"10.1049/cim2.12061","url":null,"abstract":"<p>A multi-agent iterative optimisation method based on deep reinforcement learning is proposed for the balancing and sequencing problem in mixed model assembly lines. Based on the Markov decision process model for balancing and sequencing, a balancing agent using a deep deterministic policy gradient algorithm, a sequencing agent using an Actor–Critic algorithm, as well as an iterative interaction mechanism between these agents' output solutions are designed for realising the global optimisation of mixed model assembly lines. The exchange of solution information including assembly time and station workload in the iterative interaction realises the coordination of the worker assignment policy at the balancing stage and the production arrangement policy at the sequencing stage for the minimisation of work overload and idle time at stations. Through the comparative experiments with heuristic rules, genetic algorithms, and the original deep reinforcement learning algorithm, the effectiveness of the proposed method is demonstrated and discussed for small-scale instances as well as large-scale ones.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"4 3","pages":"181-193"},"PeriodicalIF":8.2,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45671844","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}

{"title":"Construction of a semi-dense point cloud model for a tube-to-tubesheet welding robot","authors":"Hui Wang,&nbsp;Youmin Rong,&nbsp;Chao Liu,&nbsp;Yu Huang","doi":"10.1049/cim2.12056","DOIUrl":"10.1049/cim2.12056","url":null,"abstract":"<p>Tube-to-tubesheet welding is widely applied in industrial fields. However, the current tubesheet welding robot still mainly relies on manual tubesheet models. Aiming to solve this problem, this paper proposed an improved direct method to automatically establish a tubesheet semi-dense point cloud model based on a selected monocular camera and a one-dimension (1D) laser rangefinder. Firstly, a laser filtering method was designed to acquire the distance between the camera and tubesheet through the 1D laser rangefinder. Then, from combing the 1D laser rangefinder data with keyframe data, the scale factor was obtained and proceeded to be processed by the Kalman filter to reduce the error. Then, the computed scale factor and all the keyframes were calculated to construct the tubesheet point cloud model through the graph optimisation algorithm. The experimental results showed that the semi-dense point cloud model of the tubesheet could be efficiently established by the proposed algorithm with row error and column error both less than 1 mm, satisfying the welding requirements.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"4 3","pages":"220-231"},"PeriodicalIF":8.2,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42729819","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}

{"title":"Reconfigurable battery systems: Challenges and safety solutions using intelligent system framework based on digital twins","authors":"Akhil Garg,&nbsp;Jianhui Mou,&nbsp;Shaosen Su,&nbsp;Liang Gao","doi":"10.1049/cim2.12059","DOIUrl":"10.1049/cim2.12059","url":null,"abstract":"<p>Research on Reconfigurable Battery Systems (RBS) is gaining emphasis over the traditional fixed topology of the battery pack due to its advantages of adapting flexible topology (series-parallel) during its operation in the pack for meeting the non-linear time-dependent load requirements. There could emerge serious issues such as those related to safety due to malfunction of the switching circuit, heat generation from switches during frequent switching of circuits, charging temperature rise, increased charging time, sensing issues arising from the use of low-accuracy voltage/current sensors, state of charge/state of health estimation, and cost issues due to the use of increasing number of switches, fuses, contactors, relays, circuit breakers etc. To address these mentioned issues, the problem of optimal switching circuit topology for RBS is formulated as a mathematical multi-objective optimisation problem. An intelligent system framework based on digital twins is proposed. The proposed framework is further extended to a life cycle management approach that includes the interactions among pack design, pack assembly and operational and recycling levels. This could provide greater access of real-time big data cloud storage to the battery designers, manufacturers and recycling industries, who can make use of it to optimise their designs, systems and operations.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"4 3","pages":"232-248"},"PeriodicalIF":8.2,"publicationDate":"2022-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44983814","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}

{"title":"Implementation of a holistic digital twin solution for design prototyping and virtual commissioning","authors":"Miriam Ugarte Querejeta,&nbsp;Miren Illarramendi Rezabal,&nbsp;Gorka Unamuno,&nbsp;Jose Luis Bellanco,&nbsp;Eneko Ugalde,&nbsp;Antonio Valor Valor","doi":"10.1049/cim2.12058","DOIUrl":"10.1049/cim2.12058","url":null,"abstract":"<p>Industry 4.0 has ushered in a new era of digital manufacturing and in this context, digital twins are considered as the next wave of simulation technologies. The development and commissioning of Cyber Physical Systems (CPS) is taking advantage of these technologies to improve product quality while reducing costs and time to market. However, existing practices of virtual design prototyping and commissioning require the cooperation of domain specific engineering fields. This involves considerable effort as development is mostly carried out in different departments using vendor specific simulation tools. There is still no integrated simulation environment commercially available, in which all engineering disciplines can work collaboratively. This presents a major challenge when interlinking virtual models with their physical counterparts. This paper therefore addresses these challenges by implementing a holistic and vendor agnostic digital twin solution for design prototyping and commissioning practices. The solution was tested in an industrial use case, in which the digital twin effectively prototyped cost-efficient solar assembly lines.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"4 4","pages":"326-335"},"PeriodicalIF":8.2,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43305836","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}

{"title":"Special issue selected papers from International Conference of Production Research (ICPR)—Americas 2020","authors":"Daniel Alejandro Rossit,&nbsp;Diego Gabriel Rossit,&nbsp;Adrián Andrés Toncovich,&nbsp;Fernando Abel Tohmé","doi":"10.1049/cim2.12054","DOIUrl":"10.1049/cim2.12054","url":null,"abstract":"&lt;p&gt;From December 9 to 11, 2020, the “Xth International Conference of Production Research-Americas” (ICPR-Americas 2020) was held virtually in Bahía Blanca, Argentina. This conference was coordinated by a local organising committee and was sponsored by the International Foundation for Production Research. The ICPR-Americas series of conferences aim to exchange experiences and foster collaborative work among researchers and professionals from the Americas and the Caribbean region. This was the first time that the conference was held in Argentina.&lt;/p&gt;&lt;p&gt;ICPR-Americas 2020 was held in virtual mode due to the COVID-19 pandemic. Thanks to the participation and commitment of the attendees, the congress was carried out successfully, allowing many young researchers to participate in an international congress, in a year in which these opportunities were scarce. The ICPR-Americas meeting space provided them with the opportunity to share their work as well as to exchange ideas and points of view, all in the usual cordial atmosphere of the ICPR-Americas conferences.&lt;/p&gt;&lt;p&gt;The main aim of these conferences is to explore the improvement and development of production capacities and to seek knowledge about how to enhance production efficiency in a wide range of economic sectors. During the conference, a total of 245 papers were presented. More than 900 authors submitted their contributions to ICPR-Americas 2020 from different regions of the world, mainly from the Americas but also from Europe and Asia, ensuring a rich international atmosphere to the conference. The number of registrations at the conference surpassed 300. The presentations were arranged in 15 different special sessions and a central track. The authors of carefully selected papers presented at the conference were invited to extend and submit them to this Special Issue. These articles went through the journal's own reviewing process and after completing this phase, those high-quality submissions focussing on the decision-making process in production environments were selected for publication in this Special Issue.&lt;/p&gt;&lt;p&gt;In an increasingly competitive world, decision-making processes are key drivers of production systems, since they allow translating clients' demands into production actions, aiming to achieve organizational efficiency. In recent years, decision processes have been greatly enhanced by the incorporation of information technologies that allow integrating the different functionalities of the organizations, leading to more agile and flexible decision-making processes. Information technologies are useful to digitise all the information associated with the production process by ensuring the availability of this information in real time for the different sectors of companies, increasing response capacity and speeding up the decision-making processes. Moreover, the decisions and action plans generated using the information provided by the shop floor in the different business functions become ","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"4 2","pages":"71-73"},"PeriodicalIF":8.2,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41645927","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}

{"title":"Grouping technology and a hybrid genetic algorithm-desirability function approach for optimum design of cellular manufacturing systems","authors":"Atiya Al-Zuheri,&nbsp;Hussein S. Ketan,&nbsp;Ilias Vlachos","doi":"10.1049/cim2.12053","DOIUrl":"10.1049/cim2.12053","url":null,"abstract":"<p>Cell formation and machine layout in cellular manufacturing systems (CMs) design are considered as a crucial, yet hard and complex decision process. Owing to the nondeterministic polynomial time (NP) and combinatorial class of this problem, this paper presents an innovative heuristic approach to re-arrange machines enabling the minimisation of inter/intra- cellular movements as well as the cost of material handling between machines, therefore increasing group efficiency and efficacy. The heuristic approach, which is based on group technology, genetic algorithms, and desirability function, determines the optimal solution for flexible cell formation and machine layout within each cell. Flexibility refers to an explicit improvement using the desirability function to modify cell design by altering the ratio data; that is, the weight factor to meet demand flexibility. Specifically, the desirable function proposed here to provide the optimal setting of the weighting factor as a key factor which enables CMs design the flexibility to control the cell size. Promised results were obtained when the proposed approach was applied to a case study. Practical implications and recommendations are provided for use by decision makers in the design of CMs.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"4 4","pages":"267-285"},"PeriodicalIF":8.2,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48093821","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}