Interpretable deep learning approach for tool wear monitoring in high-speed milling

doi:10.1016/j.compind.2022.103638

	Interpretable deep learning approach for tool wear monitoring in high-speed milling
	Guo, Hao 1,2; Zhang, Yu 1,2; Zhu, Kunpeng 1,3
刊名	COMPUTERS IN INDUSTRY
	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.
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关键词	VIBRATION ; SIGNAL ; MODEL
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000772754500002
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
内容类型	期刊论文
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.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Huihong Bldg,Changwu Middle Rd 801, Changzhou 213164, Jiangsu, Peoples R China 2.Univ Sci & Technol China, Dept Sci Isl, Hefei 230026, Anhui, Peoples R China 3.Wuhan Univ Sci & Technol, Sch Machinery & Automat, Wuhan 430081, 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).