IET Collaborative Intelligent Manufacturing最新文献

{"title":"A nonlinear optimal control approach for the pulping process of paper mills","authors":"G. Rigatos,&nbsp;M. Abbaszadeh,&nbsp;G. Cuccurullo,&nbsp;P. Siano","doi":"10.1049/cim2.12017","DOIUrl":"10.1049/cim2.12017","url":null,"abstract":"<p>The mechanical pulping process is non-linear and multivariable. To solve the related control problem, the dynamic model of the pulping process undergoes first approximate linearization around a temporary operating point which is updated at each iteration of the control algorithm. The linearization process relies on first-order Taylor series expansion and on the computation of the Jacobian matrices of the state-space model of the pulping process. For the approximately linearized description of the pulping process, a stabilizing H-infinity feedback controller is designed. To compute the controller's feedback gains, an algebraic Riccati equation is solved at each time-step of the control method. The stability properties of the control scheme are proven through Lyapunov analysis.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"3 2","pages":"161-174"},"PeriodicalIF":8.2,"publicationDate":"2021-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48474502","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":"An integrated method for hiding sensitive association rules of the supply chains","authors":"Hui Cheng,&nbsp;Wenjie Zhang,&nbsp;Zhaoyang Wang,&nbsp;Fengjuan Zuo,&nbsp;Zaifang Zhang","doi":"10.1049/cim2.12026","DOIUrl":"10.1049/cim2.12026","url":null,"abstract":"<p>Sensitive association rule hiding is an important issue of data sharing for supply chains, which can ensure mutual benefits and avoid information leakages among different enterprises. An integrated method is proposed by using Apriori and the discrete binary particle swarm optimization (BPSO) algorithm, aiming to improve the rule hiding efficiency and effectiveness. The Apriori algorithm is used to extract the association rules from sharing data. The selected sensitive association rules can be hidden using BPSO based on constructing discrete binary space and multi-objective fitness functions. The proposed method is verified through a case study. The results show that the proposed method can effectively hide sensitive information and protect enterprises' business benefits.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"3 4","pages":"324-333"},"PeriodicalIF":8.2,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43135703","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":"Guest Editorial: Selected Papers from The International Conference on Industry 4.0 and Smart Manufacturing 2019 (ISM @SMM)","authors":"Salvatore Digiesi","doi":"10.1049/cim2.12018","DOIUrl":"10.1049/cim2.12018","url":null,"abstract":"&lt;p&gt;Industry 4.0 (I4.0) is vastly changing a complete range of industrial operations. Companies are facing new challenges but also taking advantage of the multiple opportunities offered by the Fourth Industrial Revolution. The new work environments are characterised by novel technologies and forms of organisation. When adopted, traditional models often do not provide reliable descriptions of these systems.&lt;/p&gt;&lt;p&gt;Digitalisation, enabling technologies such as machine learning, augmented and virtual reality, collaborative robots, and new forms of lean and smart manufacturing require new production and organisational models to predict and control the performance of I4.0 production systems. The availability of a great amount of data from the work environment offers the opportunity to define dynamic models that can interact with the physical environment, thus providing reliable prediction of system development and allowing for effective fulfilment of continuous improvements. The same line of reasoning applies for external environments (logistics) in the cases of both freight and human transport.&lt;/p&gt;&lt;p&gt;The adoption of new technologies and forms of organisation has the potential to spread collaborative intelligent production systems and generate circular economic processes characterised by higher levels of sustainability than those of traditional systems.&lt;/p&gt;&lt;p&gt;This Special Issue is made up of selected papers that were presented at the first International Conference on Industry 4.0 and Smart Manufacturing 2019, organised by the Modelling &amp; Simulation Center Laboratory of Enterprise Solutions, Department of Mechanical, Energy and Management Engineering of University of Calabria in Rende (Cosenza), Italy, in November 2019. The conference, attended by scientists, researchers and company managers working in smart manufacturing and I4.0, provided opportunities for attendees to network, create new synergies and collaborations, and develop new ideas for research and business. The wide range of topics covered in the conference included invited papers, tutorials and poster sessions chosen through a peer review process.&lt;/p&gt;&lt;p&gt;Following the successful conclusion of the conference, a select collection of papers from the conference were chosen for this Special Issue in &lt;i&gt;IET Collaborative Intelligent Manufacturing&lt;/i&gt;. This Special Issue is dedicated to exploring the state of the art in research and development on the adoption of enabling technologies of I4.0 in collaborative and intelligent production contexts. Papers were selected on the recommendation of a panel of experts and the authors were invited to submit their manuscripts to the journal. After extensive and independent peer review, eight significant manuscripts were accepted for publication.&lt;/p&gt;&lt;p&gt;The selected papers deal with three main topics: logistics; production systems, data-driven modelling, and support; and technological and organisational innovations for sustainability. Although the range of pa","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"3 1","pages":"1-3"},"PeriodicalIF":8.2,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47769529","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":"Knowledge map visualization of technology hotspots and development trends in China’s textile manufacturing industry","authors":"Ruihang Huang,&nbsp;Ping Yan,&nbsp;Xiaoming Yang","doi":"10.1049/cim2.12024","DOIUrl":"10.1049/cim2.12024","url":null,"abstract":"<p>The knowledge map and visualization on the technological hotspots and the developmental trends of China’s textile manufacturing industry is investigated to understand the developmental frontiers of the textile manufacturing industry technology. This work contributes to the knowledge of research and development trends of the textile manufacturing and apparel industry in a macroscopic way. The Web of Science database and the core set of the Web of Science was explored and 2852 articles in the related fields are identified from 2010 to 2019. The scientific knowledge map of the textile manufacturing technology industry is explored using CiteSpace software. For the last decade, the developmental status, research hotspots and developmental trends of the textile manufacturing and apparel industry are analysed and summarised from the perspectives of key words, hot trends and core authors. The outcomes obtained reveal that in the past 10 years, through the analysis of the technical literature of the textile manufacturing industry, different perspectives were explored where the textile manufacturing industry develops from the initial textile manufacturing treatment. The decolourisation and removal of azo dyes and other traditional textile manufacturing to the composite materials, cotton fabrics leads to the improvement of textile manufacturing wastewater treatment. Currently, the textile manufacturing industry technology has gradually developed towards an intelligent knowledge visualization and decision support. Therefore, this work suggests the developmental directions of textile manufacturing from traditional to intelligent trends, further providing a reference for the later developmental trend and the dynamic planning of China’s textile manufacturing industry technology.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"3 3","pages":"243-251"},"PeriodicalIF":8.2,"publicationDate":"2021-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47756733","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 flight control method for unmanned aerial vehicles based on vibration suppression","authors":"Xinhua Wang,&nbsp;Xiulin Zhang,&nbsp;Huajun Gong,&nbsp;Ju Jiang,&nbsp;Hari Mohan Rai","doi":"10.1049/cim2.12027","DOIUrl":"10.1049/cim2.12027","url":null,"abstract":"<p>The development of unmanned aerial vehicle (UAVs) has gained importance in recent years for various applications like photography, inspection of infrastructure and various agricultural applications. Various researchers have carried out numerous attempts at optimisation control of UAVs and their flight range maximisation. However, a few attempts have addressed structural deformation effects on the reliability and safety of UAVs. In order to study the effect of structural deformation on the reliability and safety of UAVs under normal flight conditions, a certain type of UAV as a research object is discussed and the augmented tracking model based on the original object system by using the Lyapunov function is built up. The performance is observed in terms of rise time and overshoot and it is observed from the simulation outcomes that the system is stable at all design points in the range of 0–9000 m, providing a rise time of 10 s and zero overshoot. From this study, it is revealed that the designed controller can effectively suppress the vibration of the flexible mode while ensuring the stability and tracking performance of the UAV.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"3 3","pages":"252-261"},"PeriodicalIF":8.2,"publicationDate":"2021-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44135770","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":"Improved discrete cuckoo-search algorithm for mixed no-idle permutation flow shop scheduling with consideration of energy consumption","authors":"Lingchong Zhong,&nbsp;Wenfeng Li,&nbsp;Bin Qian,&nbsp;Lijun He","doi":"10.1049/cim2.12025","DOIUrl":"10.1049/cim2.12025","url":null,"abstract":"<p>The increasing number and types of industrial tasks require factories to be more flexible in production. An improved discrete cuckoo search algorithm (CSA) is proposed and used to optimise the mixed no-idle permutation flow shop scheduling problem (MNPFSP). This problem considers MNPFSP energy consumption (MNPFSP_EC) an optimisation objective. Firstly, according to the characteristics of the individual update formula in the two stages of the standard CSA, the paper replaces the real number calculation or vector calculation in the original update formula with a discrete operation to keep the update mechanism of each stage unchanged. The change allows the algorithm to directly find a feasible solution in the discrete solution space that significantly improves the global search capability of cuckoo search. Secondly, an adaptive-starting local search based on quasi-entropy (QE) is constructed using swap, insert and 2-OPT operations with an exploitation that is adaptively executed based on QE, and QE is used to represent the diversity of population and control individuals in deciding whether to execute local search, thereby reducing computational complexity. Simulation experiments and comparisons of different instances demonstrate that the proposed algorithm can effectively solve MNPFSP_EC.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"3 4","pages":"345-355"},"PeriodicalIF":8.2,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47745134","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":"Design and development of Cu-Al-Mn-Ni shape memory alloy coated optical fibre sensor for condition-based monitoring of physical systems","authors":"Karthick Subramaniam,&nbsp;Shalini Singh,&nbsp;Sumeet Raikwar,&nbsp;Ashish Kumar Shukla,&nbsp;Iyamperumal Anand Palani","doi":"10.1049/cim2.12020","DOIUrl":"10.1049/cim2.12020","url":null,"abstract":"<p>Online fault detection, isolation and recovery using smart sensors play an important role in intelligent manufacturing system. Fibre optic sensors are very interesting for condition monitoring applications due to the advantage of this technology. Here, the experimental demonstration of Cu-based shape memory alloy (SMA) coated optical fibre for temperature-based sensing applications is reported. The benefit of Cu-based SMA coated optical fibre over conventional metallic coating has been evaluated in the study. For consistent coating, an in situ fixture with a rotary drive setup has been designed and developed. Thermo optic test bench has been developed to study the actuation characteristics of the SMA coated optical fibre for varying current and voltage. Experiments were performed to investigate the light intensity in the SMA coated optical fibre at different actuation conditions. The displacement that takes place in the optical fibre due to the external temperature stimuli will create proportional intensity and wavelength shifts. The maximum average displacement of 4.9 mm has been achieved for Cu-Al-Mn-Ni coated optical fibre. Results show variation in the optical fibre signal due to heating and cooling of the fibre from the applied electrical stimulus on Cu-based SMA coating in the form of austenite to martensite transformation.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"3 2","pages":"193-202"},"PeriodicalIF":8.2,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47089070","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":"Continuous improvement and adaptation of predictive models in smart manufacturing and model management","authors":"Florian Bachinger,&nbsp;Gabriel Kronberger,&nbsp;Michael Affenzeller","doi":"10.1049/cim2.12009","DOIUrl":"10.1049/cim2.12009","url":null,"abstract":"<p>Predictive models are increasingly deployed within smart manufacturing for the control of industrial plants. With this arises, the need for long-term monitoring of model performance and adaptation of models if surrounding conditions change and the desired prediction accuracy is no longer met. The heterogeneous landscape of application scenarios, machine learning frameworks, hardware-restricted IIoT platforms, and the diversity of enterprise systems require flexible, yet stable and error resilient solutions that allow the automated adaptation of prediction models. Recommendations are provided for the application and management of predictive models in smart manufacturing. Typical causes for concept drift in real-world smart manufacturing applications are analysed, and essential steps in data and prediction model management are highlighted, to ensure reliability and efficiency in such applications. For this purpose, recommendations and a reference architecture for model management are provided. In addition, experimental results of two model adaptation strategies on an artificial dataset are shown.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"3 1","pages":"48-63"},"PeriodicalIF":8.2,"publicationDate":"2021-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45004629","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 comprehensive framework from real-time prognostics to maintenance decisions","authors":"Amit Kumar Jain,&nbsp;Maharshi Dhada,&nbsp;Marco Perez Hernandez,&nbsp;Manuel Herrera,&nbsp;Ajith Kumar Parlikad","doi":"10.1049/cim2.12021","DOIUrl":"10.1049/cim2.12021","url":null,"abstract":"<p>Studying the influence of imperfect prognostics information on maintenance decisions is an underexplored area. To bridge this gap, a new comprehensive maintenance support system is proposed. First, a survival theory-based prognostics module employing the Weibull time-to-event recurrent neural network was deployed in which prognostics competence was enhanced by predicting the parameters of failure distribution. In conjunction with this, a new predictive maintenance (PdM) planning model was framed via a trade-off between corrective maintenance and time lost due to PdM. This optimises maintenance time based on operational and maintenance cost parameters from the historical data. The performance of the proposed framework is demonstrated using an experimental case study on maintenance planning for cutting tools within a manufacturing facility. Systematic sensitivity analysis is provided, and the impact of imperfect prognostics information on maintenance decisions is discussed. Results show that uncertainty about prediction declines as time goes on, and as uncertainty declines, the maintenance timing becomes closer to the remaining useful life. This is expected, as the risk of making a wrong decision decreases over time.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"3 2","pages":"175-183"},"PeriodicalIF":8.2,"publicationDate":"2021-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47617782","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":"Digital Twin models in industrial operations: State-of-the-art and future research directions","authors":"Tsega Y. Melesse,&nbsp;Valentina Di Pasquale,&nbsp;Stefano Riemma","doi":"10.1049/cim2.12010","DOIUrl":"10.1049/cim2.12010","url":null,"abstract":"<p>A Digital Twin is a virtual representation of a physical product, asset, process, system, or service that allows us to understand, predict, and optimise their performance for better business outcomes. Recently, the use of Digital Twin in industrial operations has attracted the attention of many scholars and industrial sectors. Despite this, there is still a need to identify its value in industrial operations mainly in production, predictive maintenance, and after-sales services. Similarly, the implementation of a Digital Twin still faces many challenges. In response, a systematic literature review and analysis of 41 papers published between 2016 and 11 July 2020 have been carried out to examine recently published works in the field. Future research directions in the area are also highlighted. The result reveals that, regardless of the challenges, the role of Digital Twin in the advancement of industrial operations, especially production and predictive maintenance is highly significant. However, its role in after-sales services remains limited. Insights are offered for research scholars, companies, and practitioners to understand the current state-of-the-art and challenges, and to indicate future research possibilities in the field.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"3 1","pages":"37-47"},"PeriodicalIF":8.2,"publicationDate":"2021-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46403459","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}