Milling Cutter Wear State Identification Method Based on Improved ResNet-34 Algorithm
Source abstract
Abstract
Tool wear state recognition is a challenging problem because the various types of signals corresponding to different wear states have similar features, making classification difficult. Convolutional neural networks are widely used in the field of tool wear state recognition. To address this problem, this study proposes a milling cutter wear state monitoring model that combines KANS and deep residual networks (ResNet). The traditional ResNet-34’s top linear classifier was replaced with a nonlinear convolutional classifier including _top_kan, and the data were preprocessed using continuous wavelet transform (CWT) to enhance the model’s immunity to interference and feature characterization. The experimental results based on the PHM dataset show that the improved KANS-ResNet-34 model improves accuracy by 1.07% compared to ResNet-34, making it comparable to ResNet-50, while its computation time is only 1/33.68 of the latter. This significantly improves computational efficiency, reduces the pressure on hardware resources, and provides an effective tool wear state recognition solution.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced machining processes and optimization · Industrial Vision Systems and Defect Detection · Advanced Machining and Optimization Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Engineering Training Center, Shenyang Aerospace University, Shenyang 110136, China
Key Laboratory of Rapid Development & Manufacturing Technology for Aircraft, Shenyang Aerospace University, Ministry of Education, Shenyang 110136, China
School of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, China
Shenyang Institute of Computing Technology Chinese Academy of Sciences, Shenyang 110168, China
University of Chinese Academy of Sciences, Beijing 100049, China
Shenyang Aircraft Company, Shenyang 110134, China
Funding
National Natural Science Commission of China · Liaoning Provincial Department of Science and Technology
References (22)
Show source-provided references
1. Tool wear intelligent monitoring techniques in cutting: A review10.1007/s12206-022-1229-9 ↗
2. The wear detection of mill-grinding tool based on acoustic emission sensor10.1007/s00170-022-09058-7 ↗
3. A novel method for online monitoring of surface quality and predicting tool wear conditions in machining of materials10.1007/s00170-022-10391-0 ↗
4. Lu, Z., Wang, M., and Dai, W. (2019). Machined surface quality monitoring using a wireless sensory tool holder in the machining process. Sensors, 19.10.3390/s19081847 ↗
5. Tool wear evaluation under minimum quantity lubrication by clustering energy of acoustic emission burst signals10.1016/j.measurement.2019.02.004 ↗
6. Tool wear estimation in end milling of titanium alloy using NPE and a novel WOA-SVM model10.1109/TIM.2019.2952476 ↗
7. Pu, X., Jia, L., Shang, K., Chen, L., Yang, T., Chen, L., Gao, L., and Qian, L. (2022). A new strategy for disc cutter wear status perception using vibration detection and machine learning. Sensors, 22.10.3390/s22176686 ↗
8. Bayesian-based uncertainty-aware tool-wear prediction model in end-milling process of titanium alloy10.1016/j.asoc.2023.110922 ↗
9. Multisensory based tool wear monitoring for practical applications in milling of titanium alloy10.1016/j.matpr.2019.12.126 ↗
10. Review of tool condition monitoring in machining and opportunities for deep learning10.1007/s00170-020-05449-w ↗
11. Indirect method of tool wear measurement and prediction using ANN network in machining process10.1016/j.matpr.2020.11.770 ↗
12. Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., and Miao, Y. (2021). Review of image classification algorithms based on convolutional neural networks. Remote Sens., 13.10.3390/rs13224712 ↗
13. Tool wear monitoring based on an improved convolutional neural network10.1007/s12206-023-0332-x ↗
14. Lin, X., Chao, S., Yan, D., Guo, L., Liu, Y., and Li, L. (2023). Multi-Sensor Data Fusion Method Based on Self-Attention Mechanism. Appl. Sci., 13.10.3390/app132111992 ↗
15. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
16. Improved depth residual network based tool wear prediction for cavity milling process10.1007/s00170-023-12829-5 ↗
17. Classification of tool wear state based on dual attention mechanism network10.1016/j.rcim.2023.102575 ↗
18. Method for edge chipping monitoring based on vibration polar coordinate image feature analysis10.1007/s00170-024-12981-6 ↗
19. Zhang, Y., Qi, X., Wang, T., and He, Y. (2023). Tool wear condition monitoring method based on deep learning with force signals. Sensors, 23.10.3390/s23104595 ↗
20. An imbalanced data learning approach for tool wear monitoring based on data augmentation
21. Lee, J., and Kwak, K. (2023). Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models. Appl. Sci., 13.10.3390/app132111942 ↗
22. Liu, Z., Wang, Y., Vaidya, S., Ruehle, F., Halverson, J., Soljačić, M., Hou, T.Y., and Tegmark, M. (2024). Kan: Kolmogorov-arnold networks. arXiv.