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

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

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 existin...

Full description

Bibliographic Details
Main Authors: Jiayu Ou, Hongkun Li, Gangjin Huang, Qiang Zhou
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Sensors
Subjects:
order analysis
stacked sparse autoencoder
spindle current signals
tool wear condition monitoring
Online Access:https://www.mdpi.com/1424-8220/20/10/2878
id doaj-2125b97e70e5493ea742dba66caf1895
record_format Article
spelling doaj-2125b97e70e5493ea742dba66caf18952020-11-25T02:58:09ZengMDPI AGSensors1424-82202020-05-01202878287810.3390/s20102878A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition MonitoringJiayu Ou0Hongkun Li1Gangjin Huang2Qiang Zhou3School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaMilling 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.https://www.mdpi.com/1424-8220/20/10/2878order analysisstacked sparse autoencoderspindle current signalstool wear condition monitoring
collection DOAJ
language English
format Article
sources DOAJ
author Jiayu Ou
Hongkun Li
Gangjin Huang
Qiang Zhou
spellingShingle Jiayu Ou
Hongkun Li
Gangjin Huang
Qiang Zhou
A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring
Sensors
order analysis
stacked sparse autoencoder
spindle current signals
tool wear condition monitoring
author_facet Jiayu Ou
Hongkun Li
Gangjin Huang
Qiang Zhou
author_sort Jiayu Ou
title A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring
title_short A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring
title_full A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring
title_fullStr A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring
title_full_unstemmed A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring
title_sort novel order analysis and stacked sparse auto-encoder feature learning method for milling tool wear condition monitoring
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2020-05-01
description 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.
topic order analysis
stacked sparse autoencoder
spindle current signals
tool wear condition monitoring
url https://www.mdpi.com/1424-8220/20/10/2878
work_keys_str_mv AT jiayuou anovelorderanalysisandstackedsparseautoencoderfeaturelearningmethodformillingtoolwearconditionmonitoring
AT hongkunli anovelorderanalysisandstackedsparseautoencoderfeaturelearningmethodformillingtoolwearconditionmonitoring
AT gangjinhuang anovelorderanalysisandstackedsparseautoencoderfeaturelearningmethodformillingtoolwearconditionmonitoring
AT qiangzhou anovelorderanalysisandstackedsparseautoencoderfeaturelearningmethodformillingtoolwearconditionmonitoring
AT jiayuou novelorderanalysisandstackedsparseautoencoderfeaturelearningmethodformillingtoolwearconditionmonitoring
AT hongkunli novelorderanalysisandstackedsparseautoencoderfeaturelearningmethodformillingtoolwearconditionmonitoring
AT gangjinhuang novelorderanalysisandstackedsparseautoencoderfeaturelearningmethodformillingtoolwearconditionmonitoring
AT qiangzhou novelorderanalysisandstackedsparseautoencoderfeaturelearningmethodformillingtoolwearconditionmonitoring
_version_ 1724708285278322688

Similar Items