An online monitoring method of milling cutter wear condition driven by digital twin
Abstract Real-time online tracking of tool wear is an indispensable element in automated machining, and tool wear directly impacts the processing quality of workpieces and overall productivity. For the milling tool wear state is difficult to real-time visualization monitoring and individual tool wea...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-55551-2 |
| _version_ | 1797274906996506624 |
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| author | Xintian Zi Shangshang Gao Yang Xie |
| author_facet | Xintian Zi Shangshang Gao Yang Xie |
| author_sort | Xintian Zi |
| collection | DOAJ |
| description | Abstract Real-time online tracking of tool wear is an indispensable element in automated machining, and tool wear directly impacts the processing quality of workpieces and overall productivity. For the milling tool wear state is difficult to real-time visualization monitoring and individual tool wear prediction model deviation is large and is not stable and so on, a digital twin-driven ensemble learning milling tool wear online monitoring novel method is proposed in this paper. Firstly, a digital twin-based milling tool wear monitoring system is built and the system model structure is clarified. Secondly, through the digital twin (DT) data multi-level processing system to optimize the signal characteristic data, combined with the ensemble learning model to predict the milling cutter wear status and wear values in real-time, the two will be verified with each other to enhance the prediction accuracy of the system. Finally, taking the milling wear experiment as an application case, the outcomes display that the predictive precision of the monitoring method is more than 96% and the prediction time is below 0.1 s, which verifies the effectiveness of the presented method, and provides a novel idea and a new approach for real-time on-line tracking of milling cutter wear in intelligent manufacturing process. |
| first_indexed | 2024-03-07T15:04:52Z |
| format | Article |
| id | doaj.art-00456a32eef3468ea26cc7fe3f41eb1a |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:04:52Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-00456a32eef3468ea26cc7fe3f41eb1a2024-03-05T18:54:51ZengNature PortfolioScientific Reports2045-23222024-02-0114111410.1038/s41598-024-55551-2An online monitoring method of milling cutter wear condition driven by digital twinXintian Zi0Shangshang Gao1Yang Xie2Taian Haina Spindle Science & Technology Co., LtdSchool of Mechanical Engineering, Jiangsu University of Science and TechnologySchool of Mechanical Engineering, Jiangsu University of Science and TechnologyAbstract Real-time online tracking of tool wear is an indispensable element in automated machining, and tool wear directly impacts the processing quality of workpieces and overall productivity. For the milling tool wear state is difficult to real-time visualization monitoring and individual tool wear prediction model deviation is large and is not stable and so on, a digital twin-driven ensemble learning milling tool wear online monitoring novel method is proposed in this paper. Firstly, a digital twin-based milling tool wear monitoring system is built and the system model structure is clarified. Secondly, through the digital twin (DT) data multi-level processing system to optimize the signal characteristic data, combined with the ensemble learning model to predict the milling cutter wear status and wear values in real-time, the two will be verified with each other to enhance the prediction accuracy of the system. Finally, taking the milling wear experiment as an application case, the outcomes display that the predictive precision of the monitoring method is more than 96% and the prediction time is below 0.1 s, which verifies the effectiveness of the presented method, and provides a novel idea and a new approach for real-time on-line tracking of milling cutter wear in intelligent manufacturing process.https://doi.org/10.1038/s41598-024-55551-2Tool wearEnsemble learningDigital twinFeature engineering |
| spellingShingle | Xintian Zi Shangshang Gao Yang Xie An online monitoring method of milling cutter wear condition driven by digital twin Scientific Reports Tool wear Ensemble learning Digital twin Feature engineering |
| title | An online monitoring method of milling cutter wear condition driven by digital twin |
| title_full | An online monitoring method of milling cutter wear condition driven by digital twin |
| title_fullStr | An online monitoring method of milling cutter wear condition driven by digital twin |
| title_full_unstemmed | An online monitoring method of milling cutter wear condition driven by digital twin |
| title_short | An online monitoring method of milling cutter wear condition driven by digital twin |
| title_sort | online monitoring method of milling cutter wear condition driven by digital twin |
| topic | Tool wear Ensemble learning Digital twin Feature engineering |
| url | https://doi.org/10.1038/s41598-024-55551-2 |
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