A Study of Tool Wear Prediction Based on Digital Twins
In the context of the global intelligent transformation of manufacturing, digital twin technology, through the deep integration of physical entities and virtual models, provides an innovative path for the implementation of smart manufacturing. Taking the VMC-C50 five-axis CNC machine tool milling ti...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2025-02-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2400 |
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| author | LIU Minghao MAO Xinhui XIA Wei YUE Caixu LIU Xianli |
| author_facet | LIU Minghao MAO Xinhui XIA Wei YUE Caixu LIU Xianli |
| author_sort | LIU Minghao |
| collection | DOAJ |
| description | In the context of the global intelligent transformation of manufacturing, digital twin technology, through the deep integration of physical entities and virtual models, provides an innovative path for the implementation of smart manufacturing. Taking the VMC-C50 five-axis CNC machine tool milling titanium alloy as the research object, a digital twin architecture-based milling tool wear monitoring system was constructed based on the technical route of “ virtual-real interaction, data-driven”. By integrating the physical perception layer, virtual modeling layer, data interconnection layer, and intelligent service layer, a bidirectional communication mechanism between the physical machine tool and the virtual model was established, achieving full-factor mapping and dynamic optimization of the machining process. With tool wear prediction as the application scenario, a deep learning model based on the fusion of multi-scale convolutional neural network, residual network, bidirectional long short-term memory network, and gated recurrent unit (MSCNN-ResNet-BiLSTM-GRU) was proposed. This model can deeply extract spatial features and dynamic temporal features, significantly improving prediction accuracy compared to conventional models. Through virtual-real interaction and data fusion mechanisms, it provides an engineering solution for the dynamic perception of tool wear during the milling process. |
| format | Article |
| id | doaj-art-e9f459ff845a41e6bb866cbf814f5177 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-e9f459ff845a41e6bb866cbf814f51772025-08-20T03:29:19ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832025-02-013001708110.15938/j.jhust.2025.01.008A Study of Tool Wear Prediction Based on Digital TwinsLIU Minghao0MAO Xinhui1XIA Wei2YUE Caixu3LIU Xianli4Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaIn the context of the global intelligent transformation of manufacturing, digital twin technology, through the deep integration of physical entities and virtual models, provides an innovative path for the implementation of smart manufacturing. Taking the VMC-C50 five-axis CNC machine tool milling titanium alloy as the research object, a digital twin architecture-based milling tool wear monitoring system was constructed based on the technical route of “ virtual-real interaction, data-driven”. By integrating the physical perception layer, virtual modeling layer, data interconnection layer, and intelligent service layer, a bidirectional communication mechanism between the physical machine tool and the virtual model was established, achieving full-factor mapping and dynamic optimization of the machining process. With tool wear prediction as the application scenario, a deep learning model based on the fusion of multi-scale convolutional neural network, residual network, bidirectional long short-term memory network, and gated recurrent unit (MSCNN-ResNet-BiLSTM-GRU) was proposed. This model can deeply extract spatial features and dynamic temporal features, significantly improving prediction accuracy compared to conventional models. Through virtual-real interaction and data fusion mechanisms, it provides an engineering solution for the dynamic perception of tool wear during the milling process.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2400digital twinscnc machine toolstool wear predictiondeep learningconvolutional neural networksbidirectional long short-term memory networks |
| spellingShingle | LIU Minghao MAO Xinhui XIA Wei YUE Caixu LIU Xianli A Study of Tool Wear Prediction Based on Digital Twins Journal of Harbin University of Science and Technology digital twins cnc machine tools tool wear prediction deep learning convolutional neural networks bidirectional long short-term memory networks |
| title | A Study of Tool Wear Prediction Based on Digital Twins |
| title_full | A Study of Tool Wear Prediction Based on Digital Twins |
| title_fullStr | A Study of Tool Wear Prediction Based on Digital Twins |
| title_full_unstemmed | A Study of Tool Wear Prediction Based on Digital Twins |
| title_short | A Study of Tool Wear Prediction Based on Digital Twins |
| title_sort | study of tool wear prediction based on digital twins |
| topic | digital twins cnc machine tools tool wear prediction deep learning convolutional neural networks bidirectional long short-term memory networks |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2400 |
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