Interpretable deep learning approach for tool wear monitoring in high-speed milling
文献类型：期刊论文
| 作者 | Guo, Hao2,3; Zhang, Yu2,3; Zhu, Kunpeng1,3 |
| 刊名 | COMPUTERS IN INDUSTRY
 |
| 出版日期 | 2022-06-01 |
| 卷号 | 138 |
| 关键词 | Tool wear monitoring Interpretability Deep learning Attention |
| ISSN号 | 0166-3615 |
| DOI | 10.1016/j.compind.2022.103638 |
| 通讯作者 | Zhu, Kunpeng(zhukp@iamt.ac.cn) |
| 英文摘要 | Tool wear monitoring (TWM) is critical in modern high-speed milling, and an effective TWM system will improve machining precision, increase tool life and reduce production costs. As a novel data-driven approach with strong learning capability, deep learning has been introduced and studied for manufacturing process monitoring, but it is rarely applied as an independent method in practice for TWM due to the poor interpretability of the monitoring results. In this study, a multi-scale pyramid attention network (MPAN) is proposed. MPAN can not only accurately monitor tool wear based on sensory signals, but also introduce the interpretability from both the aspect of network structure design and feature extraction. With the prior knowledge of signal periodicity is introduced into the structure design, the extracted multi-scale features can cover almost all the characteristic periods. In addition, the periodicity of interest can be studied based on the attention distribution. The effectiveness and feasibility of this method are verified on high-speed milling experiments. This is the first attempt to interpret deep-learning based approach for TWM.(c) 2022 Elsevier B.V. All rights reserved. |
| WOS关键词 | VIBRATION ; SIGNAL ; MODEL |
| 资助项目 | National Key Research and Development Program of China[2018YFB1703200] ; Ministry of Science and Technology of China[52175528] ; National Natural Science Foundation of China |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000772754500002 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key Research and Development Program of China ; Ministry of Science and Technology of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/128643]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Zhu, Kunpeng |
| 作者单位 | 1.Wuhan Univ Sci & Technol, Sch Machinery & Automat, Wuhan 430081, Peoples R China 2.Univ Sci & Technol China, Dept Sci Isl, Hefei 230026, Anhui, Peoples R China 3.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Huihong Bldg,Changwu Middle Rd 801, Changzhou 213164, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Hao,Zhang, Yu,Zhu, Kunpeng. Interpretable deep learning approach for tool wear monitoring in high-speed milling[J]. COMPUTERS IN INDUSTRY,2022,138. |
| APA | Guo, Hao,Zhang, Yu,&Zhu, Kunpeng.(2022).Interpretable deep learning approach for tool wear monitoring in high-speed milling.COMPUTERS IN INDUSTRY,138. |
| MLA | Guo, Hao,et al."Interpretable deep learning approach for tool wear monitoring in high-speed milling".COMPUTERS IN INDUSTRY 138(2022). |
入库方式： OAI收割
来源：合肥物质科学研究院
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