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Tool Wear Condition Monitoring Based on Improved Symmetrized Dot Pattern Enhanced Resnet18 Under Small Samples

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Udgivet i:Lubricants vol. 13, no. 11 (2025), p. 503-517
Hovedforfatter: Chen, Xiaoqin
Andre forfattere: Wang Gonghai, Fu Yuandie, Zhang, Huan, Chen, Gao
Udgivet:
MDPI AG
Fag:
Cutting force
Machining
Signal processing
Symmetrized dot pattern
Symmetry
Gray scale
Machine tools
Cutting tools
Methods
Deep learning
Time series
Optimization algorithms
Correlation coefficients
Tool wear
Condition monitoring
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Resumen:Timely and effective identification of the tool wear condition is crucial for ensuring the machining quality of CNC machine tools. In most industrial scenarios, the cost of sample collection is high, so only a small number of samples are available for model training, making it difficult for the existing tool wear condition monitoring (TCM) methods based on deep learning to achieve high performance. To address this problem, this paper proposes a TCM method based on the improved symmetric dot pattern (SDP) enhanced ResNet18. Firstly, the time series sample data is converted into grayscale matrices through SDP, the correlation coefficient between the grayscale matrices is calculated, and the optimal parameter combination of SDP is determined according to the objective of minimizing the correlation coefficient. Then, the cutting force signal is converted into a lobe diagram of the optimized SDP to enrich the sample feature information. Next, the SDP lobe diagram is input into ResNet18 for few-shot learning. The results of a series of TCM experiments demonstrate that the proposed method is significantly superior to the STFT and GAF based methods.
ISSN:2075-4442
DOI:10.3390/lubricants13110503
Fuente:Engineering Database