Comparison of Sensitivity-Guided and Black-Box Machine Tool Parameter Identification
Dynamic machine tool simulation models can be used for various applications such as process simulations, design optimization, and condition monitoring. However, all these applications require that the model replicates the real system’s behavior as accurately as possible. Next to carefully building t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Journal of Manufacturing and Materials Processing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-4494/7/4/120 |
| _version_ | 1797584250721009664 |
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| author | Johannes Ellinger Daniel Piendl Michael F. Zaeh |
| author_facet | Johannes Ellinger Daniel Piendl Michael F. Zaeh |
| author_sort | Johannes Ellinger |
| collection | DOAJ |
| description | Dynamic machine tool simulation models can be used for various applications such as process simulations, design optimization, and condition monitoring. However, all these applications require that the model replicates the real system’s behavior as accurately as possible. Next to carefully building the model, the parameterization of the model, that is, determining the parameter values the model is based upon, is the most crucial step. This paper describes the application of both sensitivity-based and black-box parameter identification to a machine tool. It further provides a comparison between these two methods and the method of sequential assembly. It is shown that both methods can increase the mode shape conformity by more than 25% and significantly reduce damping deviations. However, sensitivity-based parameter identification is the most economical method, offering the chance to update a dynamic machine tool model within minutes. |
| first_indexed | 2024-03-10T23:49:25Z |
| format | Article |
| id | doaj.art-5746d510ca8043a097f8d2e1278b813a |
| institution | Directory Open Access Journal |
| issn | 2504-4494 |
| language | English |
| last_indexed | 2024-03-10T23:49:25Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Manufacturing and Materials Processing |
| spelling | doaj.art-5746d510ca8043a097f8d2e1278b813a2023-11-19T01:44:03ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942023-06-017412010.3390/jmmp7040120Comparison of Sensitivity-Guided and Black-Box Machine Tool Parameter IdentificationJohannes Ellinger0Daniel Piendl1Michael F. Zaeh2Institute for Machine Tools and Industrial Management (<i>iwb</i>), TUM School of Engineering and Design, Technical University of Munich, Boltzmannstraße 15, 85748 Garching, GermanyInstitute for Machine Tools and Industrial Management (<i>iwb</i>), TUM School of Engineering and Design, Technical University of Munich, Boltzmannstraße 15, 85748 Garching, GermanyInstitute for Machine Tools and Industrial Management (<i>iwb</i>), TUM School of Engineering and Design, Technical University of Munich, Boltzmannstraße 15, 85748 Garching, GermanyDynamic machine tool simulation models can be used for various applications such as process simulations, design optimization, and condition monitoring. However, all these applications require that the model replicates the real system’s behavior as accurately as possible. Next to carefully building the model, the parameterization of the model, that is, determining the parameter values the model is based upon, is the most crucial step. This paper describes the application of both sensitivity-based and black-box parameter identification to a machine tool. It further provides a comparison between these two methods and the method of sequential assembly. It is shown that both methods can increase the mode shape conformity by more than 25% and significantly reduce damping deviations. However, sensitivity-based parameter identification is the most economical method, offering the chance to update a dynamic machine tool model within minutes.https://www.mdpi.com/2504-4494/7/4/120machine toolsparameter identificationsensitivity analysisoptimizationsimulationdynamics |
| spellingShingle | Johannes Ellinger Daniel Piendl Michael F. Zaeh Comparison of Sensitivity-Guided and Black-Box Machine Tool Parameter Identification Journal of Manufacturing and Materials Processing machine tools parameter identification sensitivity analysis optimization simulation dynamics |
| title | Comparison of Sensitivity-Guided and Black-Box Machine Tool Parameter Identification |
| title_full | Comparison of Sensitivity-Guided and Black-Box Machine Tool Parameter Identification |
| title_fullStr | Comparison of Sensitivity-Guided and Black-Box Machine Tool Parameter Identification |
| title_full_unstemmed | Comparison of Sensitivity-Guided and Black-Box Machine Tool Parameter Identification |
| title_short | Comparison of Sensitivity-Guided and Black-Box Machine Tool Parameter Identification |
| title_sort | comparison of sensitivity guided and black box machine tool parameter identification |
| topic | machine tools parameter identification sensitivity analysis optimization simulation dynamics |
| url | https://www.mdpi.com/2504-4494/7/4/120 |
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