Abstract
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.
Highlights
Milling cutter wear is an inevitable tool degradation phenomenon caused by mechanical, thermal, chemical and abrasive particles acting on the workpiece, which will lead to the decline of product quality and the increase of production cost [1]
Through discussing the performance of the different signals obtained from different sensors, it showed that the accuracy of the acoustic signals in the monitoring of the tool wear state had a high diagnostic accuracy of 99.6%
Parameters was Large-Broyden, Fletcher, Goldforb, Shanno (L-BFGS), which has the advantages of fast was developed for milling tool wear condition monitoring
Summary
Milling cutter wear is an inevitable tool degradation phenomenon caused by mechanical, thermal, chemical and abrasive particles acting on the workpiece, which will lead to the decline of product quality and the increase of production cost [1]. Presented a tool condition monitoring technique combining short-time Fourier transform and spectral kurtosis analysis to identify chatter process, which will guarantee the machining precision of large-size components and provide tool state information. Through discussing the performance of the different signals obtained from different sensors, it showed that the accuracy of the acoustic signals in the monitoring of the tool wear state had a high diagnostic accuracy of 99.6% It can be found from the existing literatures that most of the methods directly input the time-domain signals or frequency-domain signals into the deep learning model as sample data. A novel feature learning method called order analysis and stack sparse autoencoder (OA-SSAE) was proposed for tool wear condition monitoring.
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