@inproceedings{827df5398dcd4a59a344d6690975d9be,
title = "One-dimensional Residual Neural Network-based for Tool Wear Condition Monitoring",
abstract = "Tool wear is an essential factor affecting the machining process of a machine tool. Precisely predicting tool wear condition can not only improve machining efficiency but also effectively reduce the risk of workpiece quality degradation and machine tool damage. In recent years, deep learning has been applied to tool wear condition monitoring, and improving the accuracy of model prediction is a complicated process. In this paper, the cutting force signal was used as an input to the one-dimensional residual neural network to predict the tool wear condition. A model pre-trained method was proposed to improve the prediction accuracy, and after pre-training the model, the prediction accuracy is increased from 86.76\% to 91.16\%.",
keywords = "condition monitoring, deep learning, residual neural network (ResNet), tool wear",
author = "Dong Junjun and Dai Wei and Lu Zhiyuan",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020 ; Conference date: 16-10-2020 Through 18-10-2020",
year = "2020",
month = oct,
day = "16",
doi = "10.1109/PHM-Shanghai49105.2020.9280971",
language = "英语",
series = "2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Wei Guo and Steven Li and Qiang Miao",
booktitle = "2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020",
address = "美国",
}