Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools
The increasing demand for personalized products and the lack of skilled workers, intensified by demographic change, are major challenges for the manufacturing industry in Europe. An important framework for addressing these issues is a digital twin that represents the dynamic behavior of machine tool...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/12/2/123 |
| _version_ | 1797297659198832640 |
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| author | Florian Oexle Fabian Heimberger Alexander Puchta Jürgen Fleischer |
| author_facet | Florian Oexle Fabian Heimberger Alexander Puchta Jürgen Fleischer |
| author_sort | Florian Oexle |
| collection | DOAJ |
| description | The increasing demand for personalized products and the lack of skilled workers, intensified by demographic change, are major challenges for the manufacturing industry in Europe. An important framework for addressing these issues is a digital twin that represents the dynamic behavior of machine tools to support the remaining skilled workers and optimize processes in virtual space. Existing methods for modeling the dynamic behavior of machine tools rely on the use of expert knowledge and require a significant amount of manual effort. In this paper, a concept is proposed for individualized and lifetime-adaptive modeling of the dynamic behavior of machine tools with the focus on the machine’s tool center point. Therefore, existing and proven algorithms are combined and applied to this use case. Additionally, it eliminates the need for detailed information about the machine’s kinematic structure and utilizes automated data collection, which reduces the dependence on expert knowledge. In preliminary tests, the algorithm for the initial model setup shows a fit of 99.88% on simulation data. The introduced re-fit approach for online parameter actualization is promising, as in preliminary tests, an accuracy of 95.23% could be reached. |
| first_indexed | 2024-03-07T22:23:32Z |
| format | Article |
| id | doaj.art-280a2ec439044963aa5eac86b404c61e |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-07T22:23:32Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-280a2ec439044963aa5eac86b404c61e2024-02-23T15:25:04ZengMDPI AGMachines2075-17022024-02-0112212310.3390/machines12020123Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine ToolsFlorian Oexle0Fabian Heimberger1Alexander Puchta2Jürgen Fleischer3wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131 Karlsruhe, Germanywbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131 Karlsruhe, Germanywbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131 Karlsruhe, Germanywbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131 Karlsruhe, GermanyThe increasing demand for personalized products and the lack of skilled workers, intensified by demographic change, are major challenges for the manufacturing industry in Europe. An important framework for addressing these issues is a digital twin that represents the dynamic behavior of machine tools to support the remaining skilled workers and optimize processes in virtual space. Existing methods for modeling the dynamic behavior of machine tools rely on the use of expert knowledge and require a significant amount of manual effort. In this paper, a concept is proposed for individualized and lifetime-adaptive modeling of the dynamic behavior of machine tools with the focus on the machine’s tool center point. Therefore, existing and proven algorithms are combined and applied to this use case. Additionally, it eliminates the need for detailed information about the machine’s kinematic structure and utilizes automated data collection, which reduces the dependence on expert knowledge. In preliminary tests, the algorithm for the initial model setup shows a fit of 99.88% on simulation data. The introduced re-fit approach for online parameter actualization is promising, as in preliminary tests, an accuracy of 95.23% could be reached.https://www.mdpi.com/2075-1702/12/2/123machine tooldigital twindynamicssimulationlifecycle |
| spellingShingle | Florian Oexle Fabian Heimberger Alexander Puchta Jürgen Fleischer Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools Machines machine tool digital twin dynamics simulation lifecycle |
| title | Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools |
| title_full | Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools |
| title_fullStr | Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools |
| title_full_unstemmed | Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools |
| title_short | Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools |
| title_sort | concept for individual and lifetime adaptive modeling of the dynamic behavior of machine tools |
| topic | machine tool digital twin dynamics simulation lifecycle |
| url | https://www.mdpi.com/2075-1702/12/2/123 |
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