Milling Cutter Wear State Identification Method Based on Improved ResNet-34 Algorithm
Abstract
:1. Introduction
2. Tool Wear Monitoring Methods
2.1. Continuous Wavelet Transform of a Signal
2.2. Optimization of KAN Networks
2.3. Construction of KANS-ResNet-34 Network
3. Milling Cutter Wear Stage Identification Model
3.1. Preprocessing of Signals
3.2. Construction of Residual Network Models
3.3. Construction of Milling Cutter Wear Recognition Model
4. Experimental Verification
4.1. Description of Dataset
4.2. Experimental Result
4.2.1. Characterization of Different Signals
4.2.2. Results and Analysis
5. Conclusions
- (1)
- After transforming the original physical signals into CWT time–frequency diagrams using a continuous wavelet transform, the CWT time–frequency diagrams corresponding to each stage of wear and tear exhibited obvious differences. As the degree of wear and tear increased, the signal amplitude of the three types of signals increased, resulting in more energy being contained in the signal. This energy gradually shifted to the high-frequency portion of the signal, which was converted into a form of a picture that accurately described the signal’s local details of the time–frequency information, making it more suitable as an input to the classification network.
- (2)
- Optimizing the KAN by activating the inputs using different basis functions and then combining them linearly reduced the computational cost of the computer and significantly improved efficiency. To accommodate this optimized structure, the L1 regularization of the tensor in the original network was replaced by the L1 regularization of the weights, making the two compatible. Additionally, the parameters were initialized using the Kaiming initialization method.
- (3)
- Using the nonlinear structure of the KANS network, the include_top_kan nonlinear classifier was constructed to replace the top linear classifier in the ResNet-34 neural network. Experiments conducted on the ResNet-34 and ResNet-50 models demonstrated the feasibility of replacing the deep network model with a shallow network model, substantially saving time and guaranteeing classification accuracy. The accuracy of the KANS-ResNet-34 model reached 99.28% in the validation set and 97.79% in the test set.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Layer Name | ResNet-34 | KANS-ResNet-34 | ResNet-50 |
---|---|---|---|
Input layer | 7 × 7, 64, stride = 2 3 × 3, max pool, stride = 2 | 7 × 7, 64, stride = 2 3 × 3, max pool, stride = 2 | 7 × 7, 64, stride = 2 3 × 3, max pool, stride = 2 |
Layer [0] | |||
Layer [1] | |||
Layer [2] | |||
Layer [3] | |||
Average pool, fc, softmax | Average pool, include_top_kan, softmax | Average pool, fc, softmax |
Cutting Condition | Processing Parameter |
---|---|
Spindle speed (r/min) | 10,400 |
Feed rate (mm/min) | 1555 |
Axial cutting depth (mm) | 0.2 |
Radial cutting depth (mm) | 0.125 |
Feed per tool travel (mm) | 0.001 |
Cooling condition | Dry cut |
Class | Rec (%) | Spe (%) | Pr (%) | F1 Score (%) |
---|---|---|---|---|
Initial wear | 100 | 100 | 100 | 100 |
Normal wear | 95.12 | 100 | 100 | 97.5 |
Severe wear | 100 | 100 | 94.37 | 97.11 |
Class | Rec (%) | Spe (%) | Pr (%) | F1 Score (%) |
---|---|---|---|---|
Initial wear | 100 | 100 | 100 | 100 |
Normal wear | 93.90 | 100 | 100 | 96.85 |
Severe wear | 100 | 100 | 93.60 | 96.41 |
Class | Rec (%) | Spe (%) | Pr (%) | F1 Score (%) |
---|---|---|---|---|
Initial wear | 100 | 100 | 100 | 100 |
Normal wear | 97.56 | 100 | 100 | 98.76 |
Severe wear | 100 | 100 | 97.10 | 98.52 |
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Zheng, Y.; Chen, B.; Liu, B.; Peng, C. Milling Cutter Wear State Identification Method Based on Improved ResNet-34 Algorithm. Appl. Sci. 2024, 14, 8951. https://doi.org/10.3390/app14198951
Zheng Y, Chen B, Liu B, Peng C. Milling Cutter Wear State Identification Method Based on Improved ResNet-34 Algorithm. Applied Sciences. 2024; 14(19):8951. https://doi.org/10.3390/app14198951
Chicago/Turabian StyleZheng, Yaohui, Bolin Chen, Bengang Liu, and Chunyang Peng. 2024. "Milling Cutter Wear State Identification Method Based on Improved ResNet-34 Algorithm" Applied Sciences 14, no. 19: 8951. https://doi.org/10.3390/app14198951
APA StyleZheng, Y., Chen, B., Liu, B., & Peng, C. (2024). Milling Cutter Wear State Identification Method Based on Improved ResNet-34 Algorithm. Applied Sciences, 14(19), 8951. https://doi.org/10.3390/app14198951