Modeling and Prediction of Mixed Errors in Feed Systems Based on Digital Twins
Traditional feed system mechanism models can partially capture the characteristics of feed systems. However, to simplify the modeling process for simulation and calculation, certain factors such as component interactions and environmental influences are often neglected. These omissions lead to discr...
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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=2401 |
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| _version_ | 1849703261153525760 |
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| author | HUANG Hua MEI Le ZHI Xiaobo ZHANG Huiwang |
| author_facet | HUANG Hua MEI Le ZHI Xiaobo ZHANG Huiwang |
| author_sort | HUANG Hua |
| collection | DOAJ |
| description | Traditional feed system mechanism models can partially capture the characteristics of feed systems. However, to simplify the modeling process for simulation and calculation, certain factors such as component interactions and environmental influences are often neglected. These omissions lead to discrepancies between the mechanism model ’ s predictions and actual performance, ultimately affecting prediction accuracy. To address this issue, this study proposes a digital twin error prediction method for feed systems that integrates a mechanism model with a CNN-BiLSTM neural network. First, the feed system is abstracted as a mass spring-damping system model, taking into account the nonlinear effects of friction. Friction forces and mechanism model parameters are identified through experimental analysis. Subsequently, a neural network is employed to correct residuals between the mechanism model’s predictions and the physical system’ s actual behavior, constructing a comprehensive digital twin error prediction model. Finally, the proposed method is validated using spiral spatial trajectories. Experimental results demonstrate that the error twin model improves prediction accuracy by 76. 04% compared to traditional mechanism models and achieves superior accuracy compared to similar neural network models. This underscores the method’ s effectiveness in predicting feed system following errors, enhancing model generalization and stability, and providing a robust modeling strategy for complex systems. |
| format | Article |
| id | doaj-art-5a355336cd374403bee70dac3dce93c9 |
| institution | DOAJ |
| 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-5a355336cd374403bee70dac3dce93c92025-08-20T03:17:19ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832025-02-013001829710.15938/j.jhust.2025.01.009Modeling and Prediction of Mixed Errors in Feed Systems Based on Digital TwinsHUANG Hua0MEI Le1ZHI Xiaobo2ZHANG Huiwang3College of Mechanical and Electrical Engineering, Lanzhou University of Technology,Lanzhou 730050College of Mechanical and Electrical Engineering, Lanzhou University of Technology,Lanzhou 730050College of Mechanical and Electrical Engineering, Lanzhou University of Technology,Lanzhou 730050College of Mechanical and Electrical Engineering, Lanzhou University of Technology,Lanzhou 730050Traditional feed system mechanism models can partially capture the characteristics of feed systems. However, to simplify the modeling process for simulation and calculation, certain factors such as component interactions and environmental influences are often neglected. These omissions lead to discrepancies between the mechanism model ’ s predictions and actual performance, ultimately affecting prediction accuracy. To address this issue, this study proposes a digital twin error prediction method for feed systems that integrates a mechanism model with a CNN-BiLSTM neural network. First, the feed system is abstracted as a mass spring-damping system model, taking into account the nonlinear effects of friction. Friction forces and mechanism model parameters are identified through experimental analysis. Subsequently, a neural network is employed to correct residuals between the mechanism model’s predictions and the physical system’ s actual behavior, constructing a comprehensive digital twin error prediction model. Finally, the proposed method is validated using spiral spatial trajectories. Experimental results demonstrate that the error twin model improves prediction accuracy by 76. 04% compared to traditional mechanism models and achieves superior accuracy compared to similar neural network models. This underscores the method’ s effectiveness in predicting feed system following errors, enhancing model generalization and stability, and providing a robust modeling strategy for complex systems.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2401digital twinnumerical control machinefollowing errorfrictionfeed system |
| spellingShingle | HUANG Hua MEI Le ZHI Xiaobo ZHANG Huiwang Modeling and Prediction of Mixed Errors in Feed Systems Based on Digital Twins Journal of Harbin University of Science and Technology digital twin numerical control machine following error friction feed system |
| title | Modeling and Prediction of Mixed Errors in Feed Systems Based on Digital Twins |
| title_full | Modeling and Prediction of Mixed Errors in Feed Systems Based on Digital Twins |
| title_fullStr | Modeling and Prediction of Mixed Errors in Feed Systems Based on Digital Twins |
| title_full_unstemmed | Modeling and Prediction of Mixed Errors in Feed Systems Based on Digital Twins |
| title_short | Modeling and Prediction of Mixed Errors in Feed Systems Based on Digital Twins |
| title_sort | modeling and prediction of mixed errors in feed systems based on digital twins |
| topic | digital twin numerical control machine following error friction feed system |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2401 |
| work_keys_str_mv | AT huanghua modelingandpredictionofmixederrorsinfeedsystemsbasedondigitaltwins AT meile modelingandpredictionofmixederrorsinfeedsystemsbasedondigitaltwins AT zhixiaobo modelingandpredictionofmixederrorsinfeedsystemsbasedondigitaltwins AT zhanghuiwang modelingandpredictionofmixederrorsinfeedsystemsbasedondigitaltwins |