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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/2403.16451 |
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| _version_ | 1866908654962212864 |
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| author | Lu, Xiang-Li Hsu, Hwai-Jung Chou, Che-Wei Kung, H. T. Lee, Chen-Hsin Cheng, Sheng-Mao |
| author_facet | Lu, Xiang-Li Hsu, Hwai-Jung Chou, Che-Wei Kung, H. T. Lee, Chen-Hsin Cheng, Sheng-Mao |
| contents | We describe DeepMachining, a deep learning-based AI system for online prediction of machining errors of lathe machine operations. We have built and evaluated DeepMachining based on manufacturing data from factories. Specifically, we first pretrain a deep learning model for a given lathe machine's operations to learn the salient features of machining states. Then, we fine-tune the pretrained model to adapt to specific machining tasks. We demonstrate that DeepMachining achieves high prediction accuracy for multiple tasks that involve different workpieces and cutting tools. To the best of our knowledge, this work is one of the first factory experiments using pre-trained deep-learning models to predict machining errors of lathe machines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16451 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DeepMachining: Online Prediction of Machining Errors of Lathe Machines Lu, Xiang-Li Hsu, Hwai-Jung Chou, Che-Wei Kung, H. T. Lee, Chen-Hsin Cheng, Sheng-Mao Machine Learning Artificial Intelligence We describe DeepMachining, a deep learning-based AI system for online prediction of machining errors of lathe machine operations. We have built and evaluated DeepMachining based on manufacturing data from factories. Specifically, we first pretrain a deep learning model for a given lathe machine's operations to learn the salient features of machining states. Then, we fine-tune the pretrained model to adapt to specific machining tasks. We demonstrate that DeepMachining achieves high prediction accuracy for multiple tasks that involve different workpieces and cutting tools. To the best of our knowledge, this work is one of the first factory experiments using pre-trained deep-learning models to predict machining errors of lathe machines. |
| title | DeepMachining: Online Prediction of Machining Errors of Lathe Machines |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2403.16451 |