IET Collaborative Intelligent Manufacturing最新文献_第8页

Block workshop spatial scheduling based on cellular automata modelling and optimization 基于元胞自动机的块车间空间调度建模与优化

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2023-02-21 DOI: 10.1049/cim2.12075

Yong Chen, Xuanhao Lin, Wenchao Yi

引用次数: 0

A framework and prototype system in support of workflow collaboration and knowledge mining for manufacturing value chains 一种支持制造价值链工作流协作和知识挖掘的框架和原型系统

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2023-01-09 DOI: 10.1049/cim2.12073

Bo Qin, Peng Peng, Jian Zhang, Hongwei Wang, Ke Ma

{"title":"A framework and prototype system in support of workflow collaboration and knowledge mining for manufacturing value chains","authors":"Bo Qin, Peng Peng, Jian Zhang, Hongwei Wang, Ke Ma","doi":"10.1049/cim2.12073","DOIUrl":"10.1049/cim2.12073","url":null,"abstract":"In the field of industrial design and manufacture, computer-supported collaborative work (CSCW) systems have been widely deployed for better teamwork. However, the traditional CSCW systems have a main drawback in effectively processing and utilising knowledge across different industrial workflows. To bridge this gap, we propose a framework for collaboration between members across the manufacturing value chains to increase efficiency and reduce duplication in team cooperation. The framework contains three parts, namely workflow, knowledge mining, and services. Specifically, the workflow part provides a collaborative environment for multiple users. The knowledge mining part, as the core of the framework, extracts in-context knowledge from workflows. The part of services can interact with users with different users in each workflow, including information recommendation they need in the future or information retrieval they want to know from other workflows. Furthermore, we develop a prototype system for supporting multiple value chains collaboration to verify the effectiveness and efficiency of the framework.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"5 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41978510","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

Multi-parameters dynamic scheduling with energy management for electric vehicle charging stations 基于能量管理的电动汽车充电站多参数动态调度

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2022-12-08 DOI: 10.1049/cim2.12068

Haodong Wang, Ning Chen, Zan Liu, Songwei Zhang, Zhiguo Li, Tie Qiu

引用次数: 0

Trusted and secure composite digital twin architecture for collaborative ecosystems 用于协作生态系统的可信和安全组合数字孪生体系结构

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2022-11-24 DOI: 10.1049/cim2.12070

Pasindu Manisha Kuruppuarachchi, Susan Rea, Alan McGibney

{"title":"Trusted and secure composite digital twin architecture for collaborative ecosystems","authors":"Pasindu Manisha Kuruppuarachchi, Susan Rea, Alan McGibney","doi":"10.1049/cim2.12070","DOIUrl":"10.1049/cim2.12070","url":null,"abstract":"Digitalisation creates new opportunities for businesses to implement and manage collaborative ecosystems both internally and externally. Digital twin (DT) is a rapidly emerging technology that can be used to facilitate new models of interaction and sharing of information. DT is the digital version of a physical process or asset that can be used to model, manage, and optimise its physical counterpart. Connecting multiple DTs is vital to provide a holistic integration and view across complex ecosystems. To create a DT-based collaborative ecosystem architecture, the following concerns need to be addressed. Trust is a fundamental requirement because multiple parties will work together as part of a composite DT. Interoperability is essential, as DTs from various domains will be required to interconnect and operate seamlessly. Finally, the governance is challenging as different scenarios require various mechanisms and governance structures. This study presents an architecture to enable multiple DT-based collaborative ecosystems, and example use case scenarios to demonstrate its applicability in collaborative manufacturing.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"5 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44640196","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: 2

A hybrid model for value-added process analysis of manufacturing value chains 制造价值链增值过程分析的混合模型

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2022-11-19 DOI: 10.1049/cim2.12071

Jingwen Song, Aihui Wang, Ping Liu, Daming Li, Xiaobo Han, Yuhao Yan

{"title":"A hybrid model for value-added process analysis of manufacturing value chains","authors":"Jingwen Song, Aihui Wang, Ping Liu, Daming Li, Xiaobo Han, Yuhao Yan","doi":"10.1049/cim2.12071","DOIUrl":"10.1049/cim2.12071","url":null,"abstract":"In the digital era, realising intelligent digital transformation is a major challenge in the manufacturing field. Digital transformation means bringing more profit appreciation. To improve the analysis reliability of value-added processes, this study proposes a method for assessing enterprises value-adding activities. For this purpose, a hybrid model is constructed based on data and mathematics, bridged by a server. The research builds an element group model that identifies data from different sources, and also gives a mathematical model to describe the relationship of the supply, marketing and service. Taking an automobile manufacturing value chain as an example, to theoretically analyse the composition of value-added activities. Then, the assembly process of an automobile manufacturing plant was used as a value-added case study. The simulation results show the impact of changing production layout and product handling angle on the whole value chain. The study can provide new ideas for the intelligent digital transformation of the manufacturing industry.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"5 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49349611","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 taxonomy of factors influencing worker's performance in human–robot collaboration 人机协作中影响员工绩效的因素分类

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2022-11-11 DOI: 10.1049/cim2.12069

Valentina Di Pasquale, Valentina De Simone, Valeria Giubileo, Salvatore Miranda

{"title":"A taxonomy of factors influencing worker's performance in human–robot collaboration","authors":"Valentina Di Pasquale, Valentina De Simone, Valeria Giubileo, Salvatore Miranda","doi":"10.1049/cim2.12069","DOIUrl":"10.1049/cim2.12069","url":null,"abstract":"The occurrence of human errors significantly affects the performance and economic results of production systems. In this context, Human Reliability Analysis (HRA) methods play a key role in assessing the reliability of a man–machine system. Several HRA methods use Performance-Shaping Factors (PSFs), that is, all the aspects of human behaviour and environment that can affect human performance, to evaluate the Human Error Probability (HEP). However, despite the greater emphasis given by researchers to define of PSFs in recent years, the changes caused by the new enabling technologies implemented in manufacturing systems and derived from the Industry 4.0 paradigm have not yet been fully explored. Focussing on Human–Robot Collaboration (HRC) in production systems, the authors aim to define a PSF taxonomy that is useful for HEP evaluations in collaborative environments. To the best of the authors' knowledge, HRA approaches have not been investigated yet for HRC applications. The proposed taxonomy, which results from the integration of the most significant factors impacting workers' performance in HRC into the PSFs provided by an HRA method, can represent an important contribution for researchers and practitioners towards improving HRA methods and their applications in the context of Industry 4.0.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"5 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46019657","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: 5

Shaping the role of the digital twins for human-robot dyad: Connotations, scenarios, and future perspectives 塑造数字双胞胎在人类-机器人二元组合中的作用:内涵、场景和未来前景

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2022-10-03 DOI: 10.1049/cim2.12066

Mohaiad Elbasheer, Francesco Longo, Giovanni Mirabelli, Letizia Nicoletti, Antonio Padovano, Vittorio Solina

引用次数: 4

An improved evaluation model for supplier selection based on particle swarm optimisation-back propagation neural network 基于粒子群优化-反向传播神经网络的供应商选择改进评价模型

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2022-09-29 DOI: 10.1049/cim2.12067

Jun Yu, Daming Li, Aihui Wang, Ping Liu, Jingwen Song, Xiaobo Han

{"title":"An improved evaluation model for supplier selection based on particle swarm optimisation-back propagation neural network","authors":"Jun Yu, Daming Li, Aihui Wang, Ping Liu, Jingwen Song, Xiaobo Han","doi":"10.1049/cim2.12067","DOIUrl":"10.1049/cim2.12067","url":null,"abstract":"With the trend of supply chain globalisation, competition among enterprises is becoming more intense. Enterprises urgently need to improve their core competitiveness, and the enhancement of the competencies can depend on technologies services and the quality of suppliers. Since external factors are less controllable, this study starts with the quality of suppliers and proposes a supplier evaluation method that combines particle swarm optimisation with neural network algorithm to maximise the interests of enterprises. The particle swarm algorithm to lock the approximate location of the global optimum is first employed. Based on this, we establish an evaluation model of suppliers to train for the minimum errors between the desired and predicted values by constructing a back propagation (BP) neural network. Finally, the output results of the proposed method is compared with the BP neural network without the particle swarms optimisation. The proposed model is less empirically sensitive to the initialisation and can quickly converge to the local optimums, which overcomes the shortage of traditional neural networks and is more applicable to supplier evaluation.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"4 4","pages":"316-325"},"PeriodicalIF":8.2,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42529939","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 fast layered path planning algorithm for job shop scheduling problem 作业车间调度问题的快速分层路径规划算法

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2022-09-28 DOI: 10.1049/cim2.12065

Lin Huang, Shikui Zhao, Qing Han

{"title":"A fast layered path planning algorithm for job shop scheduling problem","authors":"Lin Huang, Shikui Zhao, Qing Han","doi":"10.1049/cim2.12065","DOIUrl":"10.1049/cim2.12065","url":null,"abstract":"Job shop scheduling problem (JSP) is a classical system resource optimisation problem and also an NP hard problem. The search algorithm based on Akers obstacle graph model is an effective algorithm to solve JSP, which first removes part of jobs from the original schedule, then constructs obstacle graph and finds the shortest path from the graph, and finally reinserts the jobs according to the shortest path decoding method to get the new schedule. Although the new scheduling can achieve good results, it is time-consuming to find the shortest path. Therefore, it is necessary to further study how to quickly plan the shortest path. This study presents a fast layered path search algorithm for solving the obstacle graph of job shop scheduling. The algorithm designs a node expansion method and a delay distance formula. The obstacles generated by different machines in the obstacle graph are layered. When the nodes expand, the extended nodes are compared with the parent layer nodes to quickly avoid closely arranged obstacles, and multiple child nodes are generated at one time through node expansion to improve the node expansion ability. At the same time, node expansion method and delay distance formula can be well integrated with A* algorithm. Finally, the test verifies that the algorithm can spend less time to find the shortest path.","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"4 4","pages":"299-315"},"PeriodicalIF":8.2,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43279717","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

State of the art on vibration signal processing towards data-driven gear fault diagnosis 振动信号处理技术在数据驱动齿轮故障诊断中的应用现状

IF 8.2

IET Collaborative Intelligent Manufacturing Pub Date : 2022-09-23 DOI: 10.1049/cim2.12064

Shouhua Zhang, Jiehan Zhou, Erhua Wang, Hong Zhang, Mu Gu, Susanna Pirttikangas

引用次数: 5