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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2409.10203 |
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| _version_ | 1866916396366036992 |
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| author | Gross, Dennis Spieker, Helge Gotlieb, Arnaud Knoblauch, Ricardo Elmansori, Mohamed |
| author_facet | Gross, Dennis Spieker, Helge Gotlieb, Arnaud Knoblauch, Ricardo Elmansori, Mohamed |
| contents | This paper presents an explainable machine learning (ML) approach for predicting surface roughness in milling. Utilizing a dataset from milling aluminum alloy 2017A, the study employs random forest regression models and feature importance techniques. The key contributions include developing ML models that accurately predict various roughness values and identifying redundant sensors, particularly those for measuring normal cutting force. Our experiments show that removing certain sensors can reduce costs without sacrificing predictive accuracy, highlighting the potential of explainable machine learning to improve cost-effectiveness in machining. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10203 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Milling Quality Prediction with Explainable Machine Learning Gross, Dennis Spieker, Helge Gotlieb, Arnaud Knoblauch, Ricardo Elmansori, Mohamed Machine Learning This paper presents an explainable machine learning (ML) approach for predicting surface roughness in milling. Utilizing a dataset from milling aluminum alloy 2017A, the study employs random forest regression models and feature importance techniques. The key contributions include developing ML models that accurately predict various roughness values and identifying redundant sensors, particularly those for measuring normal cutting force. Our experiments show that removing certain sensors can reduce costs without sacrificing predictive accuracy, highlighting the potential of explainable machine learning to improve cost-effectiveness in machining. |
| title | Efficient Milling Quality Prediction with Explainable Machine Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.10203 |