Research on Tool Wear Prediction Based on Deep Residual Network
Kai Tang, Xiaodong Wang, Wenzhi Hu, Yuan Yang
Available Online April 2018.
- 10.2991/iwmecs-18.2018.68How to use a DOI?
- Tool wear, convolutional neural network, residual network.
The convolution neural network with deep residual network structure is applied to the prediction of milling tool wear, and the high prediction accuracy is obtained. Based on the data set of the 2010 PHM data competition, this paper constructs 64000 data samples from the total life cycle wear data of three milling tools. Then a 12-layer deep residual network is trained based on these data. Compared with the classical signal analysis method, this method is more convenient for modeling and application because no signal feature extraction is needed, and the generalization ability of the model is better.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Kai Tang AU - Xiaodong Wang AU - Wenzhi Hu AU - Yuan Yang PY - 2018/04 DA - 2018/04 TI - Research on Tool Wear Prediction Based on Deep Residual Network BT - Proceedings of the 2018 3rd International Workshop on Materials Engineering and Computer Sciences (IWMECS 2018) PB - Atlantis Press SP - 314 EP - 318 SN - 2352-538X UR - https://doi.org/10.2991/iwmecs-18.2018.68 DO - 10.2991/iwmecs-18.2018.68 ID - Tang2018/04 ER -