A hybrid tool wear prediction model based on JDA
Abstract
Purpose
Aiming at solving the problems of low prediction accuracy and poor generalization caused by the difference in tool wear data distribution and the fixation of single global model parameters, a hybrid prediction modeling method for tool wear based on joint distribution adaptation (JDA) is proposed.
Design/methodology/approach
Firstly, JDA is exploited to adapt the data features with different data distributions. Then, the adapted data features are identified by the KNN classifier. Finally, according to the tool state classification results, different regression prediction models are assigned to different wear stages to complete the whole tool wear prediction task.
Findings
The results of milling experiments show that the maximum prediction accuracy of this method is 95.13%, and it has good recognition accuracy and generalization performance. Through the application of the tool wear hybrid prediction modeling method, the prediction accuracy and generalization performance of the model are improved and the tool monitoring is realized.
Originality/value
The research results can provide solutions and a theoretical basis for the application of tool wear monitoring technology in practical industrial applications.
Keywords
Acknowledgements
Financial support from the National Natural Science Foundation of China (No. 52365057, No. 51965037) is appreciated.
The web preprint entitled “A hybrid tool wear prediction model based on JDA (https://doi.org/10.21203/rs.3.rs-1679320/v1)” is appreciated.
Citation
Huang, H., Yu, W., Yao, J. and Yang, P. (2024), "A hybrid tool wear prediction model based on JDA", Engineering Computations, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/EC-08-2023-0405
Publisher
:Emerald Publishing Limited
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