Pub Date : 2023-10-05DOI: 10.1080/0951192x.2023.2264820
Qing Zheng, Guofu Ding, Haizhu Zhang, Kai Zhang, Shengfeng Qin, Shuying Wang, Wenpei Huang, Qifeng Liu
ABSTRACTThe digital twin (DT) technology facilitates the complete lifecycle management of equipment by integrating physical and virtual spaces through data mapping. Many narrative DT frameworks and modeling methods have been proposed. However, the heterogeneous processes and methods of applying these DT frameworks in different objects and different scenarios of manufacturing restricts the function and promotion of DT. Given that there are some existing discussions on narrative DT frameworks, this paper proposes an application-oriented DT framework that integrates information models, principle models, and field models. Then, the unified DT application process is discussed. The mechanism of how to fuse the multi models for typical applications in evaluation, prediction, and optimization are elaborated in detail respectively. Finally, the proposed framework and application process are validated through two DT models: vehicle wheel polygonal diagnosis digital twin and bogie frame manufacturing optimization digital twin. The correctness and feasibility of the proposed approach is demonstrated through these case studies.KEYWORDS: Equipmentapplication-oriented DT frameworkunified application processmulti-modelfusion mechanism AcknowledgementsThis work is financially supported in part by the National Key R&D Program of China (2020YFB1708000) and Natural Science Foundation of Sichuan, China (2022NSFSC1993). The authors also would like to thank Hongqin Liang and Jiaxiang Xie for providing information and data in the case study.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Key Research and Development Program of China [2020YFB1708000]; Natural Science Foundation of Sichuan Province [2022NSFSC1993].
{"title":"An application-oriented digital twin framework and the multi-model fusion mechanism","authors":"Qing Zheng, Guofu Ding, Haizhu Zhang, Kai Zhang, Shengfeng Qin, Shuying Wang, Wenpei Huang, Qifeng Liu","doi":"10.1080/0951192x.2023.2264820","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2264820","url":null,"abstract":"ABSTRACTThe digital twin (DT) technology facilitates the complete lifecycle management of equipment by integrating physical and virtual spaces through data mapping. Many narrative DT frameworks and modeling methods have been proposed. However, the heterogeneous processes and methods of applying these DT frameworks in different objects and different scenarios of manufacturing restricts the function and promotion of DT. Given that there are some existing discussions on narrative DT frameworks, this paper proposes an application-oriented DT framework that integrates information models, principle models, and field models. Then, the unified DT application process is discussed. The mechanism of how to fuse the multi models for typical applications in evaluation, prediction, and optimization are elaborated in detail respectively. Finally, the proposed framework and application process are validated through two DT models: vehicle wheel polygonal diagnosis digital twin and bogie frame manufacturing optimization digital twin. The correctness and feasibility of the proposed approach is demonstrated through these case studies.KEYWORDS: Equipmentapplication-oriented DT frameworkunified application processmulti-modelfusion mechanism AcknowledgementsThis work is financially supported in part by the National Key R&D Program of China (2020YFB1708000) and Natural Science Foundation of Sichuan, China (2022NSFSC1993). The authors also would like to thank Hongqin Liang and Jiaxiang Xie for providing information and data in the case study.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Key Research and Development Program of China [2020YFB1708000]; Natural Science Foundation of Sichuan Province [2022NSFSC1993].","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134975001","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 : 2023-10-05DOI: 10.1080/0951192x.2023.2264814
Mira Timperi, Kirsi Kokkonen, Lea Hannola
Manufacturing companies have shifted their interest toward comprehensive solutions and novel digital technologies. These technologies, such as digital twins (DTs), have enabled new business opportunities for various actors throughout product and service lifecycles, from initial drafts to disposal or reuse. However, the concreteness of these opportunities in terms of competitiveness and sustainable business still requires further study. Thus, this article examines the possibilities of data-based business and DT-enabled solutions in the manufacturing business along all stages of product-service lifecycles. The research method of this study was qualitative; it included semi-structured thematic interviews with manufacturing industry professionals. The study obtained extensive results: DT-based lifecycle solutions can provide competitiveness and sustainable value in several ways by contributing from design to usage optimization and renewal of solutions. The study also helps to understand the potential of the end of a lifecycle, as it encourages companies to assess the value of the data-based business and DT-enabled solutions throughout the entire lifecycle of a product. As a main contribution, the results inspired an updated product – service lifecycle management framework. Manufacturing companies can use this study to evaluate and recognize new business opportunities and to find ways to enhance the competitiveness and sustainability of their operations.
{"title":"Digital twins in product-service lifecycles: a framework proposal for enhancing competitiveness and sustainability in manufacturing business","authors":"Mira Timperi, Kirsi Kokkonen, Lea Hannola","doi":"10.1080/0951192x.2023.2264814","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2264814","url":null,"abstract":"Manufacturing companies have shifted their interest toward comprehensive solutions and novel digital technologies. These technologies, such as digital twins (DTs), have enabled new business opportunities for various actors throughout product and service lifecycles, from initial drafts to disposal or reuse. However, the concreteness of these opportunities in terms of competitiveness and sustainable business still requires further study. Thus, this article examines the possibilities of data-based business and DT-enabled solutions in the manufacturing business along all stages of product-service lifecycles. The research method of this study was qualitative; it included semi-structured thematic interviews with manufacturing industry professionals. The study obtained extensive results: DT-based lifecycle solutions can provide competitiveness and sustainable value in several ways by contributing from design to usage optimization and renewal of solutions. The study also helps to understand the potential of the end of a lifecycle, as it encourages companies to assess the value of the data-based business and DT-enabled solutions throughout the entire lifecycle of a product. As a main contribution, the results inspired an updated product – service lifecycle management framework. Manufacturing companies can use this study to evaluate and recognize new business opportunities and to find ways to enhance the competitiveness and sustainability of their operations.","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135436030","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 : 2023-10-04DOI: 10.1080/0951192x.2023.2264812
Yongsheng Liu, Xinhui Zhang, Kai Ding, Jizhuang Hui, Jin Zhao, Felix T.S. Chan
ABSTRACTThe machining quality of workpieces is greatly influenced by the performance of an equipment. Furthermore, it is difficult to establish an error tracing model with high tracing accuracy using a mathematical method. In this study, the machining quality of gear hubs for an automobile synchronizer produced on an intelligent manufacturing line was evaluated. The main sources of machining errors were analyzed, and the machining error tracing model for the gear hub was established through a back propagation (BP) neural network. To improve the performance of the error tracing model, the weights and thresholds of the BP neural network were optimized using the mind evolutionary algorithm (MEA). The MEA-BP error tracing model was trained and tested using online measurement results and historical data of the production line. The results showed that the average tracing accuracy of the MEA-BP method was 97.4%, which was 12.1% higher than that of the BP method. The average running time of the MEA-BP was far less than that of a genetic algorithm (GA) improved BP method. These comparisons prove that the proposed MEA-BP error tracing method is both feasible and effective. The proposed method can improve the machining quality and error tracing in intelligent manufacturing applications.KEYWORDS: Machining qualityerror tracingmind evolutionary algorithmback propagation neural networkonline measurementintelligent manufacturing Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Major Science and Technology Projects of Shaanxi Province under Grant No. 2018zdzx01-01-01 and Natural Science Foundation of Shaanxi Province under Grant Nos. 2022JM-295 and 2022JQ-576.
{"title":"A machining error tracing method based on MEA-BP neural network for quality improvement of gear hubs","authors":"Yongsheng Liu, Xinhui Zhang, Kai Ding, Jizhuang Hui, Jin Zhao, Felix T.S. Chan","doi":"10.1080/0951192x.2023.2264812","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2264812","url":null,"abstract":"ABSTRACTThe machining quality of workpieces is greatly influenced by the performance of an equipment. Furthermore, it is difficult to establish an error tracing model with high tracing accuracy using a mathematical method. In this study, the machining quality of gear hubs for an automobile synchronizer produced on an intelligent manufacturing line was evaluated. The main sources of machining errors were analyzed, and the machining error tracing model for the gear hub was established through a back propagation (BP) neural network. To improve the performance of the error tracing model, the weights and thresholds of the BP neural network were optimized using the mind evolutionary algorithm (MEA). The MEA-BP error tracing model was trained and tested using online measurement results and historical data of the production line. The results showed that the average tracing accuracy of the MEA-BP method was 97.4%, which was 12.1% higher than that of the BP method. The average running time of the MEA-BP was far less than that of a genetic algorithm (GA) improved BP method. These comparisons prove that the proposed MEA-BP error tracing method is both feasible and effective. The proposed method can improve the machining quality and error tracing in intelligent manufacturing applications.KEYWORDS: Machining qualityerror tracingmind evolutionary algorithmback propagation neural networkonline measurementintelligent manufacturing Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Major Science and Technology Projects of Shaanxi Province under Grant No. 2018zdzx01-01-01 and Natural Science Foundation of Shaanxi Province under Grant Nos. 2022JM-295 and 2022JQ-576.","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591187","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 : 2023-10-04DOI: 10.1080/0951192x.2023.2257628
L. Lestandi, J.C. Wong, G.Y. Dong, S. J. Kuehsamy, J. Mikula, G. Vastola, U. Kizhakkinan, C.S. Ford, D.W. Rosen, M.H. Dao, M.H. Jhon
ABSTRACTIn order to enable the industrialization of additive manufacturing, it is necessary to develop process simulation models that can rapidly predict part quality. Although multi-physics simulations have shown success at predicting residual stress, distortion, microstructure and mechanical properties of additively manufactured parts, they are generally too computationally expensive to be directly used in applications, such as optimization, controls, or digital twinning. In this study, a critical evaluation is made of how data-driven surrogate models can be used to model the residual stress of parts fabricated by Laser Powder-Bed Fusion. Residual stress data is generated by using an inherent-strain based process simulation for two families of part geometries. Three different models using varying levels of sophistication are compared: a multilayer perceptron (MLP), a convolutional neural network (CNN) based on the U-Net architecture, and an interpolation-based method based on mapping geometries onto a reference. All three methods were found to be sufficient for part design, providing mechanical predictions for a CPU time below 0.2 s, representing a runtime speed-up of at least 3900 × . Neural network-based models are significantly more expensive to train compared to using interpolation. However, the generality of models based on the U-Net architecture is attractive for applications in optimization.KEYWORDS: Laser Powder Bed Fusionadditive manufacturinggeometry parametrizationsurrogate modelsradial basis functionsneural network AcknowledgementsThe authors would like to thank Nagarajan Raghavan for useful discussions.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are openly available in the Mendeley data repository at http://dx.doi.org/10.17632/kkmzjr3wv7.1Additional informationFundingFinancial support was provided by the Science and Engineering Research Council, A*STAR, Singapore (Grant no. A19E1a0097).
{"title":"Data-driven surrogate modelling of residual stresses in Laser Powder-Bed Fusion","authors":"L. Lestandi, J.C. Wong, G.Y. Dong, S. J. Kuehsamy, J. Mikula, G. Vastola, U. Kizhakkinan, C.S. Ford, D.W. Rosen, M.H. Dao, M.H. Jhon","doi":"10.1080/0951192x.2023.2257628","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257628","url":null,"abstract":"ABSTRACTIn order to enable the industrialization of additive manufacturing, it is necessary to develop process simulation models that can rapidly predict part quality. Although multi-physics simulations have shown success at predicting residual stress, distortion, microstructure and mechanical properties of additively manufactured parts, they are generally too computationally expensive to be directly used in applications, such as optimization, controls, or digital twinning. In this study, a critical evaluation is made of how data-driven surrogate models can be used to model the residual stress of parts fabricated by Laser Powder-Bed Fusion. Residual stress data is generated by using an inherent-strain based process simulation for two families of part geometries. Three different models using varying levels of sophistication are compared: a multilayer perceptron (MLP), a convolutional neural network (CNN) based on the U-Net architecture, and an interpolation-based method based on mapping geometries onto a reference. All three methods were found to be sufficient for part design, providing mechanical predictions for a CPU time below 0.2 s, representing a runtime speed-up of at least 3900 × . Neural network-based models are significantly more expensive to train compared to using interpolation. However, the generality of models based on the U-Net architecture is attractive for applications in optimization.KEYWORDS: Laser Powder Bed Fusionadditive manufacturinggeometry parametrizationsurrogate modelsradial basis functionsneural network AcknowledgementsThe authors would like to thank Nagarajan Raghavan for useful discussions.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are openly available in the Mendeley data repository at http://dx.doi.org/10.17632/kkmzjr3wv7.1Additional informationFundingFinancial support was provided by the Science and Engineering Research Council, A*STAR, Singapore (Grant no. A19E1a0097).","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591616","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 : 2023-10-02DOI: 10.1080/0951192x.2023.2258090
Yuying Hu, Zewen Sheng, Min Ye, Meiyu Zhang, Chengfeng Jian
ABSTRACTDigital twin is more and more widely used, and the delivery demand of digital twin is more and more prominent at the same time of product physical delivery. Research on the digital twin product model recommendation method is of great significance for the rapid construction and reuse of digital twins. The methods currently in use, however, principally concentrate on geometric reuse and pay little attention to functional or knowledge reuse. In this paper, a graph neural network (GNN)-based deep reinforcement learning (DRL) for product model recommendation is presented. First, an MBD (model-based definition)-based semantic feature attribute adjacency graph (MSFAAG) is introduced to structured MBD model as the carrier of the digital twin product model. The MSFAAG is then embedded into continuous vector spaces using a GNN to obtain the categorization of these MBD models. Finally, DRL is used to adaptively identify more important semantic features, including manufacturing semantics and functional semantics, to obtain more detailed model classification results. The experiment effectively improves the reuse efficiency of the non-geometric aspects of the digital twin product and MBD model. Compared with other traditional recommendation algorithms, the algorithm proposed in this paper has higher accuracy and can well meet the design requirements of users.KEYWORDS: Model based definitiongraph neural networksdeep reinforcement learningreuse, recommendation AcknowledgementsThis work was supported in part by the National Natural Science Foundation of China under Grant No.61672461 and No.62073293.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China [61672461].
{"title":"GNN-based deep reinforcement learning for MBD product model recommendation","authors":"Yuying Hu, Zewen Sheng, Min Ye, Meiyu Zhang, Chengfeng Jian","doi":"10.1080/0951192x.2023.2258090","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2258090","url":null,"abstract":"ABSTRACTDigital twin is more and more widely used, and the delivery demand of digital twin is more and more prominent at the same time of product physical delivery. Research on the digital twin product model recommendation method is of great significance for the rapid construction and reuse of digital twins. The methods currently in use, however, principally concentrate on geometric reuse and pay little attention to functional or knowledge reuse. In this paper, a graph neural network (GNN)-based deep reinforcement learning (DRL) for product model recommendation is presented. First, an MBD (model-based definition)-based semantic feature attribute adjacency graph (MSFAAG) is introduced to structured MBD model as the carrier of the digital twin product model. The MSFAAG is then embedded into continuous vector spaces using a GNN to obtain the categorization of these MBD models. Finally, DRL is used to adaptively identify more important semantic features, including manufacturing semantics and functional semantics, to obtain more detailed model classification results. The experiment effectively improves the reuse efficiency of the non-geometric aspects of the digital twin product and MBD model. Compared with other traditional recommendation algorithms, the algorithm proposed in this paper has higher accuracy and can well meet the design requirements of users.KEYWORDS: Model based definitiongraph neural networksdeep reinforcement learningreuse, recommendation AcknowledgementsThis work was supported in part by the National Natural Science Foundation of China under Grant No.61672461 and No.62073293.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China [61672461].","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135898632","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 : 2023-09-30DOI: 10.1080/0951192x.2023.2263428
Miguel A. Mariscal, Sergio Ortiz Barcina, Susana García Herrero, Eva María López Perea
The use and the rapid growth of the cobot in industry are changing working conditions. New jobs can imply new advantages and inconveniences, which call for new occupational risk assessments. The aim here is to assess occupational risks in terms of mental stress, so as to determine whether a worker experiences greater stress when working in collaboration with a cobot rather than with another person while performing the same production-line process. The study involved a total of 32 volunteers of various ages, with no previous experience of cobots. An eye-tracker system that records a range of biometric data was used to quantify stress. Pupil diameter was mainly used in this investigation, as well as the number of gaze fixations by zones. The data registered were analyzed using the T-test method, with which data on two groups can be compared to test for significant differences. In addition, other secondary parameters were also analyzed, such as the time required to complete each test, and the number of errors that were committed. Among the most important conclusions, it was noted that working with cobots in no way increased stress levels, confirming one of the objectives for which these robots were designed.
{"title":"Working with collaborative robots and its influence on levels of working stress","authors":"Miguel A. Mariscal, Sergio Ortiz Barcina, Susana García Herrero, Eva María López Perea","doi":"10.1080/0951192x.2023.2263428","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2263428","url":null,"abstract":"The use and the rapid growth of the cobot in industry are changing working conditions. New jobs can imply new advantages and inconveniences, which call for new occupational risk assessments. The aim here is to assess occupational risks in terms of mental stress, so as to determine whether a worker experiences greater stress when working in collaboration with a cobot rather than with another person while performing the same production-line process. The study involved a total of 32 volunteers of various ages, with no previous experience of cobots. An eye-tracker system that records a range of biometric data was used to quantify stress. Pupil diameter was mainly used in this investigation, as well as the number of gaze fixations by zones. The data registered were analyzed using the T-test method, with which data on two groups can be compared to test for significant differences. In addition, other secondary parameters were also analyzed, such as the time required to complete each test, and the number of errors that were committed. Among the most important conclusions, it was noted that working with cobots in no way increased stress levels, confirming one of the objectives for which these robots were designed.","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136280284","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 : 2023-09-25DOI: 10.1080/0951192x.2023.2257664
Nejah Tounsi, Borhen Louhichi
ABSTRACTThis paper presents a 3D tolerance analysis approach for linear dimensions applied to planar faces in an assembly. The assembly variations are generated and visualized as an explicit geometrical stack-up of the component variations using the solid modeller Solidworks®. The feature variations are obtained by adapting the geometric solid model of each component, either by offsetting the target planar face or by tilting it within the tolerance zone. A concept of Oriented Minimum Bounding Box (OMBB) is introduced to generate individual component variations with any generalized shape of the target planar face. The analysis of the OMBB extents, the tilting angles and the corresponding pivot points has revealed symmetry in these data. Rigorous mathematical formulations have been implemented in this study to handle the general case of large and small displacements. An approach is suggested to evaluate the functional dimensions, the target face’s centroid and normal for each assembly variation. Functional dimensions of the assembly variations obtained by the software ‘3DCS Variation Analyst’ are found to deviate from those obtained by the proposed approach by up to 40% of the assembly tolerance size. 3DCS tool has also failed to detect out-of-specification assembly variations, which were identified by the proposed approach.KEYWORDS: GD&T3D Tolerancingsolid modelinglinear dimensionfeature variationassembly variations Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"Solid modelling approach for 3D tolerance analysis of linear dimension applied to planar faces in an assembly","authors":"Nejah Tounsi, Borhen Louhichi","doi":"10.1080/0951192x.2023.2257664","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257664","url":null,"abstract":"ABSTRACTThis paper presents a 3D tolerance analysis approach for linear dimensions applied to planar faces in an assembly. The assembly variations are generated and visualized as an explicit geometrical stack-up of the component variations using the solid modeller Solidworks®. The feature variations are obtained by adapting the geometric solid model of each component, either by offsetting the target planar face or by tilting it within the tolerance zone. A concept of Oriented Minimum Bounding Box (OMBB) is introduced to generate individual component variations with any generalized shape of the target planar face. The analysis of the OMBB extents, the tilting angles and the corresponding pivot points has revealed symmetry in these data. Rigorous mathematical formulations have been implemented in this study to handle the general case of large and small displacements. An approach is suggested to evaluate the functional dimensions, the target face’s centroid and normal for each assembly variation. Functional dimensions of the assembly variations obtained by the software ‘3DCS Variation Analyst’ are found to deviate from those obtained by the proposed approach by up to 40% of the assembly tolerance size. 3DCS tool has also failed to detect out-of-specification assembly variations, which were identified by the proposed approach.KEYWORDS: GD&T3D Tolerancingsolid modelinglinear dimensionfeature variationassembly variations Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135864036","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 : 2023-09-22DOI: 10.1080/0951192x.2023.2257623
Milad Ramezankhani, Mehrtash Harandi, Rudolf Seethaler, Abbas S. Milani
ABSTRACTData in advanced manufacturing are often sparse and collected from various sensory devices in a heterogeneous and multi-modal fashion. Thus, for such intricate input spaces, learning robust and reliable predictive models for product quality assessments entails implementing complex nonlinear models such as deep learning. However, these ‘data-greedy’ models require massive datasets for training, and they tend to exhibit poor generalization performance otherwise. To address the data paucity and the data heterogeneity in smart manufacturing applications, this paper introduces a sim-to-real transfer-learning framework. Specifically, using a unified wide-and-deep learning approach, the model pre-processes structured sensory data (wide) as well as high-dimensional thermal images (deep) separately, and then passes the respective concatenated features to a regressor for predicting product quality metrics. Convolutional variational autoencoder (ConvVAE) is utilized to learn concise representations of thermal images in an unsupervised fashion. ConvVAE is trained via a sim-to-real transfer learning approach, backed by theory-based heat transfer simulations. The proposed metamodeling framework was evaluated in an industrial thermoforming process case study. The results suggested that ConvVAE outperforms conventional dimensionality reduction methods despite limited data. A model explainability analysis was conducted and the resulting SHAP values demonstrated the agreement between the model’s predictions, theoretical expectations, and data correlation statistics.KEYWORDS: Intelligent manufacturingtransfer learningconvolutional variational autoencoderthermoformingmodel explainability AcknowledgementsThe authors would like to thank colleagues’ support and helpful comments at the Composites Research Network (CRN) and the University of British Columbia, especially, Mr Kurt Yesilcimen for his assistance during the data collection phase. The authors would also like to sincerely recognize the contribution of their industrial collaborator, Hytec Kohler Canada and in particular Mr Diego Faiguenbaum.Disclosure statementNo potential conflict of interest was reported by the author(s).Supplemental dataSupplemental data for this article can be accessed online at https://doi.org/10.1080/0951192X.2023.2257623Additional informationFundingThis study was financially supported by the New Frontiers in Research Fund (NFRF) of Canada – Exploration stream (award number: NFRFE-2019-01440).
{"title":"Smart manufacturing under limited and heterogeneous data: a sim-to-real transfer learning with convolutional variational autoencoder in thermoforming","authors":"Milad Ramezankhani, Mehrtash Harandi, Rudolf Seethaler, Abbas S. Milani","doi":"10.1080/0951192x.2023.2257623","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257623","url":null,"abstract":"ABSTRACTData in advanced manufacturing are often sparse and collected from various sensory devices in a heterogeneous and multi-modal fashion. Thus, for such intricate input spaces, learning robust and reliable predictive models for product quality assessments entails implementing complex nonlinear models such as deep learning. However, these ‘data-greedy’ models require massive datasets for training, and they tend to exhibit poor generalization performance otherwise. To address the data paucity and the data heterogeneity in smart manufacturing applications, this paper introduces a sim-to-real transfer-learning framework. Specifically, using a unified wide-and-deep learning approach, the model pre-processes structured sensory data (wide) as well as high-dimensional thermal images (deep) separately, and then passes the respective concatenated features to a regressor for predicting product quality metrics. Convolutional variational autoencoder (ConvVAE) is utilized to learn concise representations of thermal images in an unsupervised fashion. ConvVAE is trained via a sim-to-real transfer learning approach, backed by theory-based heat transfer simulations. The proposed metamodeling framework was evaluated in an industrial thermoforming process case study. The results suggested that ConvVAE outperforms conventional dimensionality reduction methods despite limited data. A model explainability analysis was conducted and the resulting SHAP values demonstrated the agreement between the model’s predictions, theoretical expectations, and data correlation statistics.KEYWORDS: Intelligent manufacturingtransfer learningconvolutional variational autoencoderthermoformingmodel explainability AcknowledgementsThe authors would like to thank colleagues’ support and helpful comments at the Composites Research Network (CRN) and the University of British Columbia, especially, Mr Kurt Yesilcimen for his assistance during the data collection phase. The authors would also like to sincerely recognize the contribution of their industrial collaborator, Hytec Kohler Canada and in particular Mr Diego Faiguenbaum.Disclosure statementNo potential conflict of interest was reported by the author(s).Supplemental dataSupplemental data for this article can be accessed online at https://doi.org/10.1080/0951192X.2023.2257623Additional informationFundingThis study was financially supported by the New Frontiers in Research Fund (NFRF) of Canada – Exploration stream (award number: NFRFE-2019-01440).","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136060374","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 : 2023-09-21DOI: 10.1080/0951192x.2023.2257667
Joao Paulo Jacomini Prioli, Jeremy L. Rickli
ABSTRACTDisassembly of end-of-use products is critical to the economic feasibility of circular feedstock, reusing, recycling and remanufacturing loops. High-volume disassembly operations are constrained by disassembly complexity and product variability. The capability to buffer against timing, quantity and quality uncertainties of end-of-use products impacts the efficiency and profitability of demanufacturing systems. To achieve a competitive operation in the manufacturing life-cycle, disassembly systems need automated lines, however, the unpredictability of core supply challenges automation adaptability. Disassembly robot trajectories that are programmed manually or controlled by vision systems can be time intensive and subject to variability in lighting conditions and image recognition models. Alternatively, this paper presents a novel human-robot disassembly framework to systematically extract and generate robot trajectories derived from human-collaborative robot (cobot) disassembly. The collaborative training station proposed classifies trajectory segments and then adjusts trajectories to station-specific robots in a high-volume disassembly line. Virtual and physical collaborative disassembly case studies are presented and discussed. Results demonstrate the effectiveness of the disassembly data extraction method but indicate a disparity between the expected and ideal disassembly trajectories due to variability from human handling, which is further discussed in this paper.KEYWORDS: Disassemblycollaborative robotsremanufacturingsystem framework Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work was supported by the Critical Materials Institute in collaboration with Oak Ridge National Laboratory [FA-3.3.11].
{"title":"Human-robot interaction for extraction of robotic disassembly information","authors":"Joao Paulo Jacomini Prioli, Jeremy L. Rickli","doi":"10.1080/0951192x.2023.2257667","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257667","url":null,"abstract":"ABSTRACTDisassembly of end-of-use products is critical to the economic feasibility of circular feedstock, reusing, recycling and remanufacturing loops. High-volume disassembly operations are constrained by disassembly complexity and product variability. The capability to buffer against timing, quantity and quality uncertainties of end-of-use products impacts the efficiency and profitability of demanufacturing systems. To achieve a competitive operation in the manufacturing life-cycle, disassembly systems need automated lines, however, the unpredictability of core supply challenges automation adaptability. Disassembly robot trajectories that are programmed manually or controlled by vision systems can be time intensive and subject to variability in lighting conditions and image recognition models. Alternatively, this paper presents a novel human-robot disassembly framework to systematically extract and generate robot trajectories derived from human-collaborative robot (cobot) disassembly. The collaborative training station proposed classifies trajectory segments and then adjusts trajectories to station-specific robots in a high-volume disassembly line. Virtual and physical collaborative disassembly case studies are presented and discussed. Results demonstrate the effectiveness of the disassembly data extraction method but indicate a disparity between the expected and ideal disassembly trajectories due to variability from human handling, which is further discussed in this paper.KEYWORDS: Disassemblycollaborative robotsremanufacturingsystem framework Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work was supported by the Critical Materials Institute in collaboration with Oak Ridge National Laboratory [FA-3.3.11].","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136235180","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 : 2023-09-21DOI: 10.1080/0951192x.2023.2257655
Bhaskar B. Gardas, Angappa Gunasekaran, Vaibhav S. Narwane
ABSTRACTThe Industry 4.0/smart manufacturing paradigm has significantly changed the activities and processes of organizations. Emergent smart manufacturing technology called a ‘Digital Twin’ (DT) aids organizations in enhancing overall performance by creating a virtual prototype of a real system. However, DT technology adoption in emerging economies is in the nascent stage. This research aims to identify the determinants affecting the adoption of DT technology in Indian manufacturing firms. Based on an extensive literature survey and experts’ opinions, 14 determinants were identified, and these determinants were analyzed using a hybrid multi-attribute decision-making approach to understand the contextual relationship and to identify the cause–effect relationship amongst them. Based on these results, the most critical determinants were explored, namely ‘Real-time system operations and tracking’, ‘Integration, the convergence of systems, processes & resources and enterprise collaboration’, ‘Information and Data management within or between the systems’. The manufacturing organizations of emerging economies need to consider these determinants for the effective adoption of DT technology, and policymakers can use the findings of this study to develop appropriate strategies.KEYWORDS: Information managementdigital twinsemerging economiesmanufacturing firmstechnology adoptiondecision-making Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"Unlocking factors of digital twins for smart manufacturing: a case of emerging economy","authors":"Bhaskar B. Gardas, Angappa Gunasekaran, Vaibhav S. Narwane","doi":"10.1080/0951192x.2023.2257655","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257655","url":null,"abstract":"ABSTRACTThe Industry 4.0/smart manufacturing paradigm has significantly changed the activities and processes of organizations. Emergent smart manufacturing technology called a ‘Digital Twin’ (DT) aids organizations in enhancing overall performance by creating a virtual prototype of a real system. However, DT technology adoption in emerging economies is in the nascent stage. This research aims to identify the determinants affecting the adoption of DT technology in Indian manufacturing firms. Based on an extensive literature survey and experts’ opinions, 14 determinants were identified, and these determinants were analyzed using a hybrid multi-attribute decision-making approach to understand the contextual relationship and to identify the cause–effect relationship amongst them. Based on these results, the most critical determinants were explored, namely ‘Real-time system operations and tracking’, ‘Integration, the convergence of systems, processes & resources and enterprise collaboration’, ‘Information and Data management within or between the systems’. The manufacturing organizations of emerging economies need to consider these determinants for the effective adoption of DT technology, and policymakers can use the findings of this study to develop appropriate strategies.KEYWORDS: Information managementdigital twinsemerging economiesmanufacturing firmstechnology adoptiondecision-making Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136236097","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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