Milling Tool Wear Prediction Method Based on Deep Learning Under Variable Working Conditions
Tool wear prediction is essential to ensure part quality and machining efficiency. Tool wear is affected by factors such as the material, structure, process, and processing time of the parts. Tool wear under the variable working conditions and the above factors show a complex coupling and timing cor...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/620c6b92ee5146e18691fe0ee1546192 |
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