A Cutting Mechanics-based Machine Learning Modeling Method to Discover Governing Equations of Machining Dynamics
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912802145304576 |
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| author | Ren, Alisa Ma, Mason Wu, Jiajie Karandikar, Jaydeep Tyler, Chris Shi, Tony Schmitz, Tony |
| author_facet | Ren, Alisa Ma, Mason Wu, Jiajie Karandikar, Jaydeep Tyler, Chris Shi, Tony Schmitz, Tony |
| contents | This paper proposes a cutting mechanics-based machine learning (CMML) modeling method to discover governing equations of machining dynamics. The main idea of CMML design is to integrate existing physics in cutting mechanics and unknown physics in data to achieve automated model discovery, with the potential to advance machining modeling. Based on existing physics in cutting mechanics, CMML first establishes a general modeling structure governing machining dynamics, that is represented by a set of unknown differential algebraic equations. CMML can therefore achieve data-driven discovery of these unknown equations through effective cutting mechanics-based nonlinear learning function space design and discrete optimization-based learning algorithm. Experimentally verified time domain simulation of milling is used to validate the proposed modeling method. Numerical results show CMML can discover the exact milling dynamics models with process damping and edge force from noisy data. This indicates that CMML has the potential to be used for advancing machining modeling in practice with the development of effective metrology systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14817 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Cutting Mechanics-based Machine Learning Modeling Method to Discover Governing Equations of Machining Dynamics Ren, Alisa Ma, Mason Wu, Jiajie Karandikar, Jaydeep Tyler, Chris Shi, Tony Schmitz, Tony Machine Learning Computational Engineering, Finance, and Science This paper proposes a cutting mechanics-based machine learning (CMML) modeling method to discover governing equations of machining dynamics. The main idea of CMML design is to integrate existing physics in cutting mechanics and unknown physics in data to achieve automated model discovery, with the potential to advance machining modeling. Based on existing physics in cutting mechanics, CMML first establishes a general modeling structure governing machining dynamics, that is represented by a set of unknown differential algebraic equations. CMML can therefore achieve data-driven discovery of these unknown equations through effective cutting mechanics-based nonlinear learning function space design and discrete optimization-based learning algorithm. Experimentally verified time domain simulation of milling is used to validate the proposed modeling method. Numerical results show CMML can discover the exact milling dynamics models with process damping and edge force from noisy data. This indicates that CMML has the potential to be used for advancing machining modeling in practice with the development of effective metrology systems. |
| title | A Cutting Mechanics-based Machine Learning Modeling Method to Discover Governing Equations of Machining Dynamics |
| topic | Machine Learning Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2501.14817 |