Abstract
It is costly to predict tool wear under various machining conditions. To address this challenge, a tool cyber-physical prediction (TCPP) scheme and a hybrid dynamic neural network (HDNN) model are proposed in this paper. This scheme enables users to build and use the models both in the cloud and at the factory by integrating the theoretical maximum tool life and the practical sensing features of the tool wear. Moreover, using features extracted from the sensors and controller, the HDNN model integrates the logistic regression and dynamic neural network to diagnose the tool break and predict tool wear simultaneously. In addition, the scheme presents a model-refreshing approach to tune the HDNN model to adapt to physical variation of the tool coating, the workpiece material, and the removal process in the similar cutting conditions. The experimental results demonstrate that the TCPP scheme with the HDNN model is promising for tool wear prediction while using only a few samples and the current features to adapt to various cutting conditions.
| Original language | English |
|---|---|
| Pages (from-to) | 614-625 |
| Number of pages | 12 |
| Journal | Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an |
| Volume | 40 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 2017 Oct 3 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
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A cyber-physical scheme for predicting tool wear based on a hybrid dynamic neural network. / Yang, Haw Ching; Li, Yu Yung; Hung, Min Hsiung; Cheng, Fan Tien.
In: Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an, Vol. 40, No. 7, 03.10.2017, p. 614-625.Research output: Contribution to journal › Article
TY - JOUR
T1 - A cyber-physical scheme for predicting tool wear based on a hybrid dynamic neural network
AU - Yang, Haw Ching
AU - Li, Yu Yung
AU - Hung, Min Hsiung
AU - Cheng, Fan Tien
PY - 2017/10/3
Y1 - 2017/10/3
N2 - It is costly to predict tool wear under various machining conditions. To address this challenge, a tool cyber-physical prediction (TCPP) scheme and a hybrid dynamic neural network (HDNN) model are proposed in this paper. This scheme enables users to build and use the models both in the cloud and at the factory by integrating the theoretical maximum tool life and the practical sensing features of the tool wear. Moreover, using features extracted from the sensors and controller, the HDNN model integrates the logistic regression and dynamic neural network to diagnose the tool break and predict tool wear simultaneously. In addition, the scheme presents a model-refreshing approach to tune the HDNN model to adapt to physical variation of the tool coating, the workpiece material, and the removal process in the similar cutting conditions. The experimental results demonstrate that the TCPP scheme with the HDNN model is promising for tool wear prediction while using only a few samples and the current features to adapt to various cutting conditions.
AB - It is costly to predict tool wear under various machining conditions. To address this challenge, a tool cyber-physical prediction (TCPP) scheme and a hybrid dynamic neural network (HDNN) model are proposed in this paper. This scheme enables users to build and use the models both in the cloud and at the factory by integrating the theoretical maximum tool life and the practical sensing features of the tool wear. Moreover, using features extracted from the sensors and controller, the HDNN model integrates the logistic regression and dynamic neural network to diagnose the tool break and predict tool wear simultaneously. In addition, the scheme presents a model-refreshing approach to tune the HDNN model to adapt to physical variation of the tool coating, the workpiece material, and the removal process in the similar cutting conditions. The experimental results demonstrate that the TCPP scheme with the HDNN model is promising for tool wear prediction while using only a few samples and the current features to adapt to various cutting conditions.
UR - http://www.scopus.com/inward/record.url?scp=85031290148&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85031290148&partnerID=8YFLogxK
U2 - 10.1080/02533839.2017.1372223
DO - 10.1080/02533839.2017.1372223
M3 - Article
AN - SCOPUS:85031290148
VL - 40
SP - 614
EP - 625
JO - Chung-kuo Kung Ch'eng Hsueh K'an/Journal of the Chinese Institute of Engineers
JF - Chung-kuo Kung Ch'eng Hsueh K'an/Journal of the Chinese Institute of Engineers
SN - 0253-3839
IS - 7
ER -