ABSTRACTThe concept of Industry 5.0 (I5.0) promotes the human-centricity as the core value behind the evolution of smart manufacturing systems (SMSs), based on a novel use of digital technologies in the design and management of modern industrial systems to take up the socio-technical challenges. In this context, the paper proposes a Smart Manufacturing Systems Design (SMSD) framework enabling I5.0, based on the human-automation symbiosis. Thanks to an ‘Augmented Digital Twin’ (ADT) able to integrate and digitize all the entities of the factory (i.e. machines, robots, environments, interfaces, people), AI-driven applications can be built to support the user domain and make people and machines co-evolve thanks to a systematic data sharing between physical and digital assets (e.g. digital twin, virtual mock-ups, human-machine interfaces), optimizing factory productivity and workers wellbeing. In this framework, machines and humans can both generate knowledge and learn from each other, generating a virtuous co-evolution, supporting the understanding of the human-machine interplay and the creation of an effective collaboration between people and SMSs. The framework was conceived and validated involving four industrial companies, belonging to diverse sectors, interested in overcoming the current limits of I4.0 lines by including the human factors for future SMS management.KEYWORDS: Industry 5.0Operator 4.0Operator 5.0augmented digital twinsmart manufacturing systemshuman-automation symbiosis AcknowledgementsThis research is funded by the European Community under two HORIZON 2020 programmes, grant agreement No. 958303 (PeneloPe) https://penelope-project.eu/ and grant agreement No. 101091780 (DaCapo) https://www.dacapo-project.eu/.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the H2020 Industrial Leadership [958303].
{"title":"A framework to design smart manufacturing systems for Industry 5.0 based on the human-automation symbiosis","authors":"Margherita Peruzzini, Elisa Prati, Marcello Pelicciari","doi":"10.1080/0951192x.2023.2257634","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257634","url":null,"abstract":"ABSTRACTThe concept of Industry 5.0 (I5.0) promotes the human-centricity as the core value behind the evolution of smart manufacturing systems (SMSs), based on a novel use of digital technologies in the design and management of modern industrial systems to take up the socio-technical challenges. In this context, the paper proposes a Smart Manufacturing Systems Design (SMSD) framework enabling I5.0, based on the human-automation symbiosis. Thanks to an ‘Augmented Digital Twin’ (ADT) able to integrate and digitize all the entities of the factory (i.e. machines, robots, environments, interfaces, people), AI-driven applications can be built to support the user domain and make people and machines co-evolve thanks to a systematic data sharing between physical and digital assets (e.g. digital twin, virtual mock-ups, human-machine interfaces), optimizing factory productivity and workers wellbeing. In this framework, machines and humans can both generate knowledge and learn from each other, generating a virtuous co-evolution, supporting the understanding of the human-machine interplay and the creation of an effective collaboration between people and SMSs. The framework was conceived and validated involving four industrial companies, belonging to diverse sectors, interested in overcoming the current limits of I4.0 lines by including the human factors for future SMS management.KEYWORDS: Industry 5.0Operator 4.0Operator 5.0augmented digital twinsmart manufacturing systemshuman-automation symbiosis AcknowledgementsThis research is funded by the European Community under two HORIZON 2020 programmes, grant agreement No. 958303 (PeneloPe) https://penelope-project.eu/ and grant agreement No. 101091780 (DaCapo) https://www.dacapo-project.eu/.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the H2020 Industrial Leadership [958303].","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136308111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-19DOI: 10.1080/0951192x.2023.2257632
Kombaya Touckia Jesus
ABSTRACTToday, faced with the global COVID-19 crisis, manufacturing systems are subject to constraints caused by an uncertain and changing environment dominated by strong international competition. In this context, many indicators have been proposed to evaluate the responsiveness and flexibility of production systems. The literature review shows that some research streams have received positive attention from the research community, these streams include RMS characteristics analysis, RMS performance analysis and applied research and field applications, while other streams such as the reconfigurability level assessment and reconfigurability towards Industry 4.0, still need further research. This paper shows the need for more rigorous analytical measures to assess the level of reconfigurability, as there are still no accurate and quantitative RMS reconfigurability indices. There is a need for successful case studies detailing best practices to effectively guide the transition of modern industrial enterprises towards reconfigurable manufacturing. This paper proposes, a decision support tool to help manufacturers evaluate reconfigurability according to its characteristics (modularity, scalability, integrability, convertibility, diagnosability and customization) using fuzzy logic.KEYWORDS: Reconfigurable manufacturing system (RMS)decision-makingfuzzy logicDEMATELMAUT Disclosure statementNo potential conflict of interest was reported by the author(s).Availability of data and materialThe authors confirm that the data and material supporting the findings of this work are available within the article. The raw data that support the findings of this study are available from the corresponding author, upon a reasonable request.Ethical approvalThe authors declare compliance with ethical standards.Additional informationFundingThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
{"title":"Evaluation of the reconfigurability of manufacturing systems based on fuzzy logic taking into account the links between the characteristics of the RMS","authors":"Kombaya Touckia Jesus","doi":"10.1080/0951192x.2023.2257632","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257632","url":null,"abstract":"ABSTRACTToday, faced with the global COVID-19 crisis, manufacturing systems are subject to constraints caused by an uncertain and changing environment dominated by strong international competition. In this context, many indicators have been proposed to evaluate the responsiveness and flexibility of production systems. The literature review shows that some research streams have received positive attention from the research community, these streams include RMS characteristics analysis, RMS performance analysis and applied research and field applications, while other streams such as the reconfigurability level assessment and reconfigurability towards Industry 4.0, still need further research. This paper shows the need for more rigorous analytical measures to assess the level of reconfigurability, as there are still no accurate and quantitative RMS reconfigurability indices. There is a need for successful case studies detailing best practices to effectively guide the transition of modern industrial enterprises towards reconfigurable manufacturing. This paper proposes, a decision support tool to help manufacturers evaluate reconfigurability according to its characteristics (modularity, scalability, integrability, convertibility, diagnosability and customization) using fuzzy logic.KEYWORDS: Reconfigurable manufacturing system (RMS)decision-makingfuzzy logicDEMATELMAUT Disclosure statementNo potential conflict of interest was reported by the author(s).Availability of data and materialThe authors confirm that the data and material supporting the findings of this work are available within the article. The raw data that support the findings of this study are available from the corresponding author, upon a reasonable request.Ethical approvalThe authors declare compliance with ethical standards.Additional informationFundingThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135060504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-18DOI: 10.1080/0951192x.2023.2257652
Luis Velazquez, Genevieve Palardy, Corina Barbalata
ABSTRACTThis paper presents a robotic 3D printer specifically designed for ultraviolet (UV)-curable thermosets, whose printing parameters can be selected by using a predictive modeling strategy. A specialized extruder head was designed and integrated with a UR5e robotic arm. Software packages were developed to enable the communication between the extruder head and the robotic arm, and control systems were implemented to regulate the printing process. A predictive approach using either a feedforward neural network (FNN) or convolution neural network (CNN) is proposed for estimating the dimensions of future prints based on the process parameters. This enables selection of the appropriate parameters for high-quality prints. This strategy aims to decrease expensive trial-and-error campaigns for material and printing parameter tuning. Experimental results demonstrate the capabilities of the robotic 3D printer and the accuracy of the predictive approach.KEYWORDS: UV-curable thermosetsrobotic systemadditive manufacturingmachine learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Louisiana Board of Regents [LEQSF-EPS(2022)-LAMDASeed-Track1B-11]; Louisiana Board of Regents [LEQSF-EPS(2021)-LAMDASeed-Track1B-01]; Office of Integrative Activities [OIA1946231].
{"title":"A robotic 3D printer for UV-curable thermosets: dimensionality prediction using a data-driven approach","authors":"Luis Velazquez, Genevieve Palardy, Corina Barbalata","doi":"10.1080/0951192x.2023.2257652","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257652","url":null,"abstract":"ABSTRACTThis paper presents a robotic 3D printer specifically designed for ultraviolet (UV)-curable thermosets, whose printing parameters can be selected by using a predictive modeling strategy. A specialized extruder head was designed and integrated with a UR5e robotic arm. Software packages were developed to enable the communication between the extruder head and the robotic arm, and control systems were implemented to regulate the printing process. A predictive approach using either a feedforward neural network (FNN) or convolution neural network (CNN) is proposed for estimating the dimensions of future prints based on the process parameters. This enables selection of the appropriate parameters for high-quality prints. This strategy aims to decrease expensive trial-and-error campaigns for material and printing parameter tuning. Experimental results demonstrate the capabilities of the robotic 3D printer and the accuracy of the predictive approach.KEYWORDS: UV-curable thermosetsrobotic systemadditive manufacturingmachine learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Louisiana Board of Regents [LEQSF-EPS(2022)-LAMDASeed-Track1B-11]; Louisiana Board of Regents [LEQSF-EPS(2021)-LAMDASeed-Track1B-01]; Office of Integrative Activities [OIA1946231].","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135148674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-18DOI: 10.1080/0951192x.2023.2257620
Fuqiang Zhang, Fengli Xu, Xueliang Zhou, Kai Ding, Shujun Shao, Chao Du, Jiewu Leng
ABSTRACTModels that predict tool life based on wear mechanism knowledge are typically inaccurate, as the use of simplified model parameters can have a significant effect on this prediction. While a tool life prediction model based on sample cutting data is limited to specific working conditions, which makes tool life prediction difficult to generalize, and needs a large amount of historical data as support. In this paper, the empirical formula of tool life based on wear mechanism knowledge was combined with a neural network, which can significantly improve prediction accuracy. Firstly, a concept of tool life grade is proposed, and its classification standard is outlined. Secondly, a prediction model based on the empirical life formula and experimental data was established. Thirdly, a tool wear prediction model based on a convolutional neural network (CNN) was established through the real-time tool condition data, and the corresponding life compensation strategy can be determined by comparing this with the historical data. Finally, the empirical life grade was adjusted to obtain the real-time tool life grade. A case example shows that the data-driven knowledge-guided prediction model can significantly improve the recognition accuracy of tool life grade.KEYWORDS: Milling tool life gradewear mechanism knowledgecondition dataconvolutional neural networkreal time prediction AcknowledgementsThis work was supported in part by the National Key R&D Program of China (2021YFB3301702), Major Special Science and Technology Project of Shaanxi Province, China (No.2018zdzx01-01-01), and the Natural Science Foundation of Shaanxi Province, China (No. 2021JM-173).Disclosure statementNo potential conflict of interest was reported by the authors.Contribution StatementFuqiang Zhang provided the research idea; Fengli Xu wrote the paper and developed a software testing system; Xueliang Zhou and Jiew Leng conducted review and editing; Kai Ding provided the funding acquisition; Shujun Shao and Chao Du provided the data set.Additional informationFundingThe work was supported by the National Key R&D Program of China [2021YFB3301702]; Natural Science Foundation of Shaanxi Province, China [2021JM-173]; Major Special Science and Technology Project of Shaanxi Province, China [2018zdzx01-01-01].
{"title":"<sup>Data-</sup> <sup>driven</sup> <sup>and</sup> <sup>knowledge-</sup> <sup>guided prediction model of milling tool life</sup> <sup>grade</sup>","authors":"Fuqiang Zhang, Fengli Xu, Xueliang Zhou, Kai Ding, Shujun Shao, Chao Du, Jiewu Leng","doi":"10.1080/0951192x.2023.2257620","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257620","url":null,"abstract":"ABSTRACTModels that predict tool life based on wear mechanism knowledge are typically inaccurate, as the use of simplified model parameters can have a significant effect on this prediction. While a tool life prediction model based on sample cutting data is limited to specific working conditions, which makes tool life prediction difficult to generalize, and needs a large amount of historical data as support. In this paper, the empirical formula of tool life based on wear mechanism knowledge was combined with a neural network, which can significantly improve prediction accuracy. Firstly, a concept of tool life grade is proposed, and its classification standard is outlined. Secondly, a prediction model based on the empirical life formula and experimental data was established. Thirdly, a tool wear prediction model based on a convolutional neural network (CNN) was established through the real-time tool condition data, and the corresponding life compensation strategy can be determined by comparing this with the historical data. Finally, the empirical life grade was adjusted to obtain the real-time tool life grade. A case example shows that the data-driven knowledge-guided prediction model can significantly improve the recognition accuracy of tool life grade.KEYWORDS: Milling tool life gradewear mechanism knowledgecondition dataconvolutional neural networkreal time prediction AcknowledgementsThis work was supported in part by the National Key R&D Program of China (2021YFB3301702), Major Special Science and Technology Project of Shaanxi Province, China (No.2018zdzx01-01-01), and the Natural Science Foundation of Shaanxi Province, China (No. 2021JM-173).Disclosure statementNo potential conflict of interest was reported by the authors.Contribution StatementFuqiang Zhang provided the research idea; Fengli Xu wrote the paper and developed a software testing system; Xueliang Zhou and Jiew Leng conducted review and editing; Kai Ding provided the funding acquisition; Shujun Shao and Chao Du provided the data set.Additional informationFundingThe work was supported by the National Key R&D Program of China [2021YFB3301702]; Natural Science Foundation of Shaanxi Province, China [2021JM-173]; Major Special Science and Technology Project of Shaanxi Province, China [2018zdzx01-01-01].","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135154408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-15DOI: 10.1080/0951192x.2023.2257648
Samuel Omole, Hakan Dogan, Alexander J G Lunt, Simon Kirk, Alborz Shokrani
Machining of single-phase tungsten, used as a plasma facing material in fusion energy reactors, is commonly associated with rapid tool wear and short tool life. Conventional methods of monitoring tool wear or changing cutting tools after a predetermined period are inefficient and can lead to unnecessary tool change or risk damaging the workpiece. Tool wear can adversely affect the surface finish and dimensional tolerances of machined parts. Predicting its onset can avoid this critical damage whilst ensuring maximum tool life is utilised. In this paper, firstly the tool life results in end milling single-phase tungsten using different cutting tool geometries and cutting speeds are provided for the first time. A novel method is proposed by combining sensor signal prediction and classification machine learning models. It works by forecasting the cutting tool bending moment signal which is then used for predicting future cutting tool condition in end milling of pure dense tungsten. A series of machining experiments, covering the whole life of a cutting tool, were performed to collect the sensor signals. The current time series signal from the sensory tool holder is employed to forecast the future signal by training a 1D convolutional neural network (1D CNN) and an artificial neural network (ANN). The forecasted signal is then used to predict the state of the cutting tool in the future. Machine learning classifiers namely, random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost) supervised learning models were trained and validated on actual sensor signals to correlate the tool conditions with specific sensor signal features. The investigations revealed that the 1D CNN performed best in forecasting the time series sensor signal whilst achieving a mean absolute error of 3.37. In addition, the RF, when trained on Wavelet Scattering features, resulted in the most accurate classification of sensor signals for tool condition detection. The analysis showed that the combination of 1D CNN signal forecasting, feature extraction through statistical analyses and RF classifier performs best in predicting the state of a cutting tool in near future. Using this method allows for decision making for changing the tool whilst ensuring that the maximum useful life of a cutting tool is utilised. It also enables preventing undesired damage to the machined surface due to late detection of tool wear or delays in taking appropriate actions. The application of this method can reliably reduce the manufacturing costs and resource consumption associated with cutting tools for machining tungsten and minimise tool wear induced damage to the workpiece.
{"title":"Using machine learning for cutting tool condition monitoring and prediction during machining of tungsten","authors":"Samuel Omole, Hakan Dogan, Alexander J G Lunt, Simon Kirk, Alborz Shokrani","doi":"10.1080/0951192x.2023.2257648","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257648","url":null,"abstract":"Machining of single-phase tungsten, used as a plasma facing material in fusion energy reactors, is commonly associated with rapid tool wear and short tool life. Conventional methods of monitoring tool wear or changing cutting tools after a predetermined period are inefficient and can lead to unnecessary tool change or risk damaging the workpiece. Tool wear can adversely affect the surface finish and dimensional tolerances of machined parts. Predicting its onset can avoid this critical damage whilst ensuring maximum tool life is utilised. In this paper, firstly the tool life results in end milling single-phase tungsten using different cutting tool geometries and cutting speeds are provided for the first time. A novel method is proposed by combining sensor signal prediction and classification machine learning models. It works by forecasting the cutting tool bending moment signal which is then used for predicting future cutting tool condition in end milling of pure dense tungsten. A series of machining experiments, covering the whole life of a cutting tool, were performed to collect the sensor signals. The current time series signal from the sensory tool holder is employed to forecast the future signal by training a 1D convolutional neural network (1D CNN) and an artificial neural network (ANN). The forecasted signal is then used to predict the state of the cutting tool in the future. Machine learning classifiers namely, random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost) supervised learning models were trained and validated on actual sensor signals to correlate the tool conditions with specific sensor signal features. The investigations revealed that the 1D CNN performed best in forecasting the time series sensor signal whilst achieving a mean absolute error of 3.37. In addition, the RF, when trained on Wavelet Scattering features, resulted in the most accurate classification of sensor signals for tool condition detection. The analysis showed that the combination of 1D CNN signal forecasting, feature extraction through statistical analyses and RF classifier performs best in predicting the state of a cutting tool in near future. Using this method allows for decision making for changing the tool whilst ensuring that the maximum useful life of a cutting tool is utilised. It also enables preventing undesired damage to the machined surface due to late detection of tool wear or delays in taking appropriate actions. The application of this method can reliably reduce the manufacturing costs and resource consumption associated with cutting tools for machining tungsten and minimise tool wear induced damage to the workpiece.","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135436856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-15DOI: 10.1080/0951192x.2023.2257665
Jr-Fong Dang
ABSTRACTThe emergence of Industry 4.0 has led to the development of modern production machines that are usually equipped with advanced sensors to collect data for further analysis. This study proposes a deep learning-based framework to perform equipment health monitoring (EHM) and further broadens its applicability through the integration of subject matter expert (SME) knowledge. A sliding window strategy was adopted to perform EHM in real time. Moreover, an autocorrelation function (ACF) and a partial autocorrelation function (PACF) were employed to determine the optimal window size based on the elbow method. An empirical study was conducted to demonstrate the effectiveness and practicality of the proposed framework. Furthermore, to provide better prediction results, an optimal combination of hyperparameters that minimized the loss function and further validated the window size obtained by the ACF and PACF was determined and used. The results showed that the proposed algorithm outperformed other representative machine learning models. Finally, a general framework was adopted to maintain equipment performance.KEYWORDS: Deep learningequipment health monitoringsliding windowautocorrelation function (ACF)partial autocorrelation function (PACF)subject matter expert (SME) AcknowledgementsThe author would like to acknowledge a very good collection of data from Hsiang-Po Tsai and the support from Hong-Yi Huang.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Ministry of Science and Technology of Taiwan under Grant 109-2222-E-035-007- and 110-2221-E-005-087-.
{"title":"The deep learning-based equipment health monitoring model adopting subject matter expert","authors":"Jr-Fong Dang","doi":"10.1080/0951192x.2023.2257665","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257665","url":null,"abstract":"ABSTRACTThe emergence of Industry 4.0 has led to the development of modern production machines that are usually equipped with advanced sensors to collect data for further analysis. This study proposes a deep learning-based framework to perform equipment health monitoring (EHM) and further broadens its applicability through the integration of subject matter expert (SME) knowledge. A sliding window strategy was adopted to perform EHM in real time. Moreover, an autocorrelation function (ACF) and a partial autocorrelation function (PACF) were employed to determine the optimal window size based on the elbow method. An empirical study was conducted to demonstrate the effectiveness and practicality of the proposed framework. Furthermore, to provide better prediction results, an optimal combination of hyperparameters that minimized the loss function and further validated the window size obtained by the ACF and PACF was determined and used. The results showed that the proposed algorithm outperformed other representative machine learning models. Finally, a general framework was adopted to maintain equipment performance.KEYWORDS: Deep learningequipment health monitoringsliding windowautocorrelation function (ACF)partial autocorrelation function (PACF)subject matter expert (SME) AcknowledgementsThe author would like to acknowledge a very good collection of data from Hsiang-Po Tsai and the support from Hong-Yi Huang.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Ministry of Science and Technology of Taiwan under Grant 109-2222-E-035-007- and 110-2221-E-005-087-.","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135395166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.1080/0951192x.2023.2257650
Yunrui Wang, Yao Wang, Wengzhe Ren, Zhengli Wu, Juan Li
ABSTRACTThe uncertainty and dynamic changes in assembly resources can seriously affect the normal operation of the assembly plant. In response to the problems of incomprehensive resource control mechanism, poor timeliness of monitoring data, and low level of scheduling intelligence in the assembly plant, a dynamic scheduling and perception method of assembly resources based on digital twin is proposed so that the uncertainties in the assembly process can be monitored and dealt with in time. In this paper, a dynamic scheduling model of assembly resources based on a digital twin is constructed, and the operation mechanism of assembly resources in the constructed digital twin model is expounded. And the dynamic perception method of assembly resources based on the Petri network is studied in detail, and the perception and interaction models of four assembly resources in the product assembly process are constructed: workpiece, handling equipment, assembly center, and storage area. Finally, combined with the assembly workshop of enterprise A’s frame factory, the Petri network model is simulated with the help of the CPN Tools simulation tool to obtain real-time and simulation data such as assembly resources and workstation operation time are obtained, which provides a scientific basis for the smooth implementation of enterprise assembly plan and dynamic scheduling of assembly resources.KEYWORDS: Petri netdigital twinassembly resourcesdynamic perception Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"Research on dynamic scheduling and perception method of assembly resources based on digital twin","authors":"Yunrui Wang, Yao Wang, Wengzhe Ren, Zhengli Wu, Juan Li","doi":"10.1080/0951192x.2023.2257650","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257650","url":null,"abstract":"ABSTRACTThe uncertainty and dynamic changes in assembly resources can seriously affect the normal operation of the assembly plant. In response to the problems of incomprehensive resource control mechanism, poor timeliness of monitoring data, and low level of scheduling intelligence in the assembly plant, a dynamic scheduling and perception method of assembly resources based on digital twin is proposed so that the uncertainties in the assembly process can be monitored and dealt with in time. In this paper, a dynamic scheduling model of assembly resources based on a digital twin is constructed, and the operation mechanism of assembly resources in the constructed digital twin model is expounded. And the dynamic perception method of assembly resources based on the Petri network is studied in detail, and the perception and interaction models of four assembly resources in the product assembly process are constructed: workpiece, handling equipment, assembly center, and storage area. Finally, combined with the assembly workshop of enterprise A’s frame factory, the Petri network model is simulated with the help of the CPN Tools simulation tool to obtain real-time and simulation data such as assembly resources and workstation operation time are obtained, which provides a scientific basis for the smooth implementation of enterprise assembly plan and dynamic scheduling of assembly resources.KEYWORDS: Petri netdigital twinassembly resourcesdynamic perception Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134911702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.1080/0951192x.2023.2257668
Elisa Reboredo, Pedro Espadinha-Cruz
ABSTRACTCurrently, organizations in the manufacturing sector are exposed to high levels of competition and constant changes in consumer requirements. To survive in an increasingly competitive environment, it is essential to adopt new technologies to ensure success in a sector that is currently going through the fourth industrial revolution, associated with Industry 4.0 (I4.0). Additive manufacturing (AM) is one of I4.0’s technologies, which can produce products layer by layer, playing a key role in the innovation of business models. In this manner, organizations must integrate AM starting by understanding their maturity level, which allows them to reflect on weaknesses and strengths as well as opportunities for improvement. However, literature is currently lacking maturity models for AM. This paper proposes a maturity model for AM, which aims to help organizations in the manufacturing sector in determining their maturity level regarding the implementation of the AM. The model developed was validated theoretically. Also, a case study was conducted on an automaker, where it was possible to conclude that, despite the analyzed company has a high level of maturity regarding the technological deployment of AM, an AM’s strategy definition is presently missing.KEYWORDS: Additive manufacturing3D printingindustry 4.0maturity model Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the Fundação para a Ciência e a Tecnologia [UIDB/00667/2020 (UNIDEMI)].
{"title":"Proposal of a maturity model for additive manufacturing: theoretical development and case study in automotive industry","authors":"Elisa Reboredo, Pedro Espadinha-Cruz","doi":"10.1080/0951192x.2023.2257668","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257668","url":null,"abstract":"ABSTRACTCurrently, organizations in the manufacturing sector are exposed to high levels of competition and constant changes in consumer requirements. To survive in an increasingly competitive environment, it is essential to adopt new technologies to ensure success in a sector that is currently going through the fourth industrial revolution, associated with Industry 4.0 (I4.0). Additive manufacturing (AM) is one of I4.0’s technologies, which can produce products layer by layer, playing a key role in the innovation of business models. In this manner, organizations must integrate AM starting by understanding their maturity level, which allows them to reflect on weaknesses and strengths as well as opportunities for improvement. However, literature is currently lacking maturity models for AM. This paper proposes a maturity model for AM, which aims to help organizations in the manufacturing sector in determining their maturity level regarding the implementation of the AM. The model developed was validated theoretically. Also, a case study was conducted on an automaker, where it was possible to conclude that, despite the analyzed company has a high level of maturity regarding the technological deployment of AM, an AM’s strategy definition is presently missing.KEYWORDS: Additive manufacturing3D printingindustry 4.0maturity model Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the Fundação para a Ciência e a Tecnologia [UIDB/00667/2020 (UNIDEMI)].","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134910615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.1080/0951192x.2023.2258111
Lorenzo Scalera, Carlo Nainer, Andrea Giusti, Alessandro Gasparetto
In this paper, an approach for computing online safety zones for collaborative robotics in a robust way, despite uncertain robot dynamics, is proposed. The strategy implements the speed and separation monitoring paradigm, and considers human and robot enclosed in bounding volumes. The human-robot collaboration is monitored by a supervisory controller that guides the robot to stop along a path-consistent trajectory in case of collision danger between human and robot. The size of the robot safety zone is minimized online according to the stop time of the manipulator, and the uncertain robot dynamics is considered using interval arithmetic to ensure compliance with the joint torques limits even in case of imperfect knowledge of the dynamic model parameters. The results verify the effectiveness of the proposed approach, and evaluate the influence of dynamics variations on human-robot collaboration.
{"title":"Robust safety zones for manipulators with uncertain dynamics in collaborative robotics","authors":"Lorenzo Scalera, Carlo Nainer, Andrea Giusti, Alessandro Gasparetto","doi":"10.1080/0951192x.2023.2258111","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2258111","url":null,"abstract":"In this paper, an approach for computing online safety zones for collaborative robotics in a robust way, despite uncertain robot dynamics, is proposed. The strategy implements the speed and separation monitoring paradigm, and considers human and robot enclosed in bounding volumes. The human-robot collaboration is monitored by a supervisory controller that guides the robot to stop along a path-consistent trajectory in case of collision danger between human and robot. The size of the robot safety zone is minimized online according to the stop time of the manipulator, and the uncertain robot dynamics is considered using interval arithmetic to ensure compliance with the joint torques limits even in case of imperfect knowledge of the dynamic model parameters. The results verify the effectiveness of the proposed approach, and evaluate the influence of dynamics variations on human-robot collaboration.","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134910616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.1080/0951192x.2023.2257661
Joao Henrique Cavalcanti, Tibor Kovacs, Andrea Ko, Károly Pocsarovszky
Industry 4.0 seeks waste reduction via the optimization of production systems integrating technology and process. In addition to evaluating existing methods and technologies, academia also develops new ones. This research proposes a new hybrid artificial intelligence (AI) solution for production system efficiency optimization that combines data envelopment analysis (DEA), machine learning (ML)-based simulation and genetic algorithms (GAs) using real-world sensor data from a thermoelectric power plant. In the proposed method, DEA is employed to identify the production system’s efficient frontier, which is used to build an ML model that predicts production efficiency through simulation. A genetic algorithm is then utilized to propose those settings that result in optimized production efficiency. Although the possibility of combining DEA-ML and ML-GA has been discussed in the literature, no research was found that combines these three methods for production efficiency optimization. The proposed solution was tested and validated using real-world data. The benefits of the hybrid AI solution were measured by comparing its predicted efficiency with the efficiencies achieved by running production with conventional control-loops based control systems. The results show that considerable efficiency improvement can be achieved using the proposed hybrid AI solution.
{"title":"Production system efficiency optimization through application of a hybrid artificial intelligence solution","authors":"Joao Henrique Cavalcanti, Tibor Kovacs, Andrea Ko, Károly Pocsarovszky","doi":"10.1080/0951192x.2023.2257661","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257661","url":null,"abstract":"Industry 4.0 seeks waste reduction via the optimization of production systems integrating technology and process. In addition to evaluating existing methods and technologies, academia also develops new ones. This research proposes a new hybrid artificial intelligence (AI) solution for production system efficiency optimization that combines data envelopment analysis (DEA), machine learning (ML)-based simulation and genetic algorithms (GAs) using real-world sensor data from a thermoelectric power plant. In the proposed method, DEA is employed to identify the production system’s efficient frontier, which is used to build an ML model that predicts production efficiency through simulation. A genetic algorithm is then utilized to propose those settings that result in optimized production efficiency. Although the possibility of combining DEA-ML and ML-GA has been discussed in the literature, no research was found that combines these three methods for production efficiency optimization. The proposed solution was tested and validated using real-world data. The benefits of the hybrid AI solution were measured by comparing its predicted efficiency with the efficiencies achieved by running production with conventional control-loops based control systems. The results show that considerable efficiency improvement can be achieved using the proposed hybrid AI solution.","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134911710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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