IEICE TRANSACTIONS on Information
Intelligent Tool Condition Monitoring Based on Multi-Scale Convolutional Recurrent Neural Network
Summary :
Tool condition monitoring is one of the core tasks of intelligent manufacturing in digital workshop. This paper presents an intelligent recognize method of tool condition based on deep learning. First, the industrial microphone is used to collect the acoustic signal during machining; then, a central fractal decomposition algorithm is proposed to extract sensitive information; finally, the multi-scale convolutional recurrent neural network is used for deep feature extraction and pattern recognition. The multi-process milling experiments proved that the proposed method is superior to the existing methods, and the recognition accuracy reached 88%.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.644-652
- Publication Date
- 2023/05/01
- Publicized
- 2022/06/16
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022DLP0043
- Type of Manuscript
- Special Section PAPER (Special Section on Deep Learning Technologies: Architecture, Optimization, Techniques, and Applications)
- Category
- Smart Industry
Authors
Xincheng CAO
Xiamen University
Bin YAO
Xiamen University
Binqiang CHEN
Xiamen University
Wangpeng HE
Xidian University
Suqin GUO
Fujian Great Power Science and Technology Co., Ltd
Kun CHEN
Fujian Great Power Science and Technology Co., Ltd
Keyword
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Xincheng CAO, Bin YAO, Binqiang CHEN, Wangpeng HE, Suqin GUO, Kun CHEN, "Intelligent Tool Condition Monitoring Based on Multi-Scale Convolutional Recurrent Neural Network" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 644-652, May 2023, doi: 10.1587/transinf.2022DLP0043.
Abstract: Tool condition monitoring is one of the core tasks of intelligent manufacturing in digital workshop. This paper presents an intelligent recognize method of tool condition based on deep learning. First, the industrial microphone is used to collect the acoustic signal during machining; then, a central fractal decomposition algorithm is proposed to extract sensitive information; finally, the multi-scale convolutional recurrent neural network is used for deep feature extraction and pattern recognition. The multi-process milling experiments proved that the proposed method is superior to the existing methods, and the recognition accuracy reached 88%.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022DLP0043/_p
Copy
@ARTICLE{e106-d_5_644,
author={Xincheng CAO, Bin YAO, Binqiang CHEN, Wangpeng HE, Suqin GUO, Kun CHEN, },
journal={IEICE TRANSACTIONS on Information},
title={Intelligent Tool Condition Monitoring Based on Multi-Scale Convolutional Recurrent Neural Network},
year={2023},
volume={E106-D},
number={5},
pages={644-652},
abstract={Tool condition monitoring is one of the core tasks of intelligent manufacturing in digital workshop. This paper presents an intelligent recognize method of tool condition based on deep learning. First, the industrial microphone is used to collect the acoustic signal during machining; then, a central fractal decomposition algorithm is proposed to extract sensitive information; finally, the multi-scale convolutional recurrent neural network is used for deep feature extraction and pattern recognition. The multi-process milling experiments proved that the proposed method is superior to the existing methods, and the recognition accuracy reached 88%.},
keywords={},
doi={10.1587/transinf.2022DLP0043},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Intelligent Tool Condition Monitoring Based on Multi-Scale Convolutional Recurrent Neural Network
T2 - IEICE TRANSACTIONS on Information
SP - 644
EP - 652
AU - Xincheng CAO
AU - Bin YAO
AU - Binqiang CHEN
AU - Wangpeng HE
AU - Suqin GUO
AU - Kun CHEN
PY - 2023
DO - 10.1587/transinf.2022DLP0043
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - Tool condition monitoring is one of the core tasks of intelligent manufacturing in digital workshop. This paper presents an intelligent recognize method of tool condition based on deep learning. First, the industrial microphone is used to collect the acoustic signal during machining; then, a central fractal decomposition algorithm is proposed to extract sensitive information; finally, the multi-scale convolutional recurrent neural network is used for deep feature extraction and pattern recognition. The multi-process milling experiments proved that the proposed method is superior to the existing methods, and the recognition accuracy reached 88%.
ER -
English
