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Open AccessArticle

A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring

School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
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Sensors 2020, 20(10), 2878; https://doi.org/10.3390/s20102878
Received: 21 April 2020 / Revised: 9 May 2020 / Accepted: 14 May 2020 / Published: 19 May 2020
(This article belongs to the Section Fault Diagnosis & Sensors)
Milling is a main processing mode of the modern manufacturing industry, which seriously affects the quality and precision of the machined workpiece. However, it is difficult to monitor the tool wear condition in the continuous cutting process, especially under a variable speed condition. The existing tool wear condition monitoring methods only carry out analysis with a constant engine speed. Different from the general monitoring methods, this paper put forward a milling cutter wear condition monitoring method based on order analysis (OA) and stacked sparse autoencoder (SSAE). The methodology in the research include signals feature extraction and tool wear state monitoring and were designed to analyze the three-phase spindle current signals instead of the traditional force signals and vibration signals. The variable speed signals were transformed into angle domain stationary signals by order analysis, and the SSAE neural network was used to monitor the tool wear state. The proposed method was verified on the laboratory signals and the results showed a better performance than the other methods and a better applicability in actual industrial manufacturing. View Full-Text
Keywords: order analysis; stacked sparse autoencoder; spindle current signals; tool wear condition monitoring order analysis; stacked sparse autoencoder; spindle current signals; tool wear condition monitoring
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Ou, J.; Li, H.; Huang, G.; Zhou, Q. A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring. Sensors 2020, 20, 2878.

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