Process data based estimation of tool wear on punching machines using TCN-Autoencoder from raw time-series information

Tracking the wear states of tools on punching machines is necessary to reduce scrap rates. In this paper, we propose a method to estimate wear state of punches using Temporal Convolutional Network Autoencoder (TCN-Autoencoder), one of the deep learning techniques for learning time-series information...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering Vol. 1157; no. 1; pp. 12078 - 12086
Main Authors: Asahi, Shota, Karadogan, Celalettin, Tamura, Satoshi, Hayamizu, Satoru, Liewald, Mathias
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.06.2021
Subjects:
Anomalies
Audio data
Deep learning
Errors
Machine learning
Punches
Reconstruction
Tool wear
ISSN:1757-8981, 1757-899X
Online Access:Get full text
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Summary:Tracking the wear states of tools on punching machines is necessary to reduce scrap rates. In this paper, we propose a method to estimate wear state of punches using Temporal Convolutional Network Autoencoder (TCN-Autoencoder), one of the deep learning techniques for learning time-series information with convolutional architecture. Approach involves inputting raw time-series information, such as sensor, vibration and audio data, into TCN-Autoencoder, and calculating the reconstruction error between the output and the input data. The reconstruction error is used as “anomaly score” and indicates the distance from the normal state. By training TCN-Autoencoder only with data annotated as “normal” state, the reconstruction error becomes larger when inputting abnormal state data, which corresponds the wear state of the punch. Performance is evaluated on experimental measurement data that spans various wear states of the punch. The results showed our model can estimate anomalies faster than the conventional machine-learning-based anomaly estimation method, while maintaining the high estimation accuracy. This is due to TCN-Autoencoder being able to learn from both frequency and time domain.
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ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/1157/1/012078