Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Tool Condition Monitoring Model Based on DAE–SVR
AU - Sun, Xiaoning
AU - Yang, Zhifeng
AU - Xia, Maojin
AU - Xia, Min
AU - Liu, Changfu
AU - Zhou, Yang
AU - Guo, Yuquan
PY - 2025/2/1
Y1 - 2025/2/1
N2 - Cutting tools are executive components in metal processing, and tool wear directly affects the quality of the workpiece and processing efficiency; monitoring the change in its state is crucial to avoid accidents and ensure the safety of workers. The traditional monitoring model cannot compress a large amount of cutting data effectively, failing to obtain reliable feature data, and there are some defects in generalization ability and monitoring accuracy. For this purpose, this article takes milling cutters as the research object, and it integrates signals from force sensors, vibration sensors, and acoustic emission sensors, combining the advantages of the denoising autoencoder (DAE) model in data compression and the high monitoring accuracy of the support vector regression (SVR) model, to establish a tool wear monitoring model based on DAE–SVR. The results show that compared with traditional DAE and SVR models in multiple datasets, the maximum improvement in monitoring performance (MAE) is 43.58%.
AB - Cutting tools are executive components in metal processing, and tool wear directly affects the quality of the workpiece and processing efficiency; monitoring the change in its state is crucial to avoid accidents and ensure the safety of workers. The traditional monitoring model cannot compress a large amount of cutting data effectively, failing to obtain reliable feature data, and there are some defects in generalization ability and monitoring accuracy. For this purpose, this article takes milling cutters as the research object, and it integrates signals from force sensors, vibration sensors, and acoustic emission sensors, combining the advantages of the denoising autoencoder (DAE) model in data compression and the high monitoring accuracy of the support vector regression (SVR) model, to establish a tool wear monitoring model based on DAE–SVR. The results show that compared with traditional DAE and SVR models in multiple datasets, the maximum improvement in monitoring performance (MAE) is 43.58%.
U2 - 10.3390/machines13020115
DO - 10.3390/machines13020115
M3 - Journal article
VL - 13
JO - Machines
JF - Machines
SN - 2075-1702
IS - 2
M1 - 115
ER -