A Novel Multivariate Cutting Force-Based Tool Wear Monitoring Method Using One-Dimensional Convolutional Neural Network
Abstract
:1. Introduction
2. Methodologies
2.1. Multivariate Variational Mode Decomposition
2.2. The Proposed Modified Multiscale Permutation Entropy
2.3. One-Dimensional Convolutional Neural Network
- (1)
- Convolutional Layer
- (2)
- Pooling Layer
- (3)
- Batch Normalization
3. A Novel Multivariate Cutting Force-Based Tool Wear Monitoring Method Using One-Dimensional Convolutional Neural Network Is Proposed
4. Application Research by Experimental Data Analysis
4.1. Experimental Data Description
4.2. Quantitative Feature Extraction Based on Multivariate Cutting Force Signals
4.3. Tool Wear Condition Monitoring by One-Dimensional Convolutional Neural Network
5. Conclusions and Discussion
- (1)
- Multivariate cutting force signals were used as monitoring signals to realize tool wear monitoring in this paper. Multivariate cutting force signals contain comprehensive dynamic information on tool wear, which is suitable for extracting wear characteristics. At the same time, the research on multiple signals agrees with the rapid development trend of multi-sensor acquisition systems.
- (2)
- MVMD and MMPE were combined to extract the characteristic information of tool wear. MVMD can decompose multivariate cutting force signals adaptively and can effectively separate the frequency components of multiple signals. MMPE can accurately characterize the nonlinear characteristics of tool wear as condition indicators.
- (3)
- 1D CNN has strong adaptive feature extraction ability, which can reduce the error of empirical judgment and make the recognition effect more accurate and intelligent. Compared with the traditional machine learning model, 1D CNN has higher recognition ability and better monitoring effects.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Operational Parameter | Value |
---|---|
CNC milling machine | Roders Tech RFM760 |
Dynamometer | Kistler 9265B |
Spindle speed | 10,400 r/min |
Feed rate | 1555 mm/min |
Z depth of cut (axial) | 0.2 mm |
Y depth of cut (radial) | 0.125 mm |
Sampling frequency | 50 kHz |
Wear Condition | Wear Label | Number of Millings |
---|---|---|
Initial wear | C1 | 1~81 |
Normal wear | C2 | 82~188 |
Severe wear | C3 | 189~315 |
Methods | Average Classification Accuracy |
---|---|
VMD + MMPE + 1D CNN (X-axis) | 92.91% |
VMD + MMPE + 1D CNN (Z-axis) | 93.23% |
VMD + MMPE + 1D CNN (Y-axis) | 94.03% |
1D CNN | 93.28% |
MVMD + MPE + 1D CNN | 95.48% |
MEMD + MMPE + 1D CNN | 96.61% |
MVMD + MMPE + GA-SVM | 96.77% |
MVMD + MMPE + 1D CNN | 97.42% |
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Yang, X.; Yuan, R.; Lv, Y.; Li, L.; Song, H. A Novel Multivariate Cutting Force-Based Tool Wear Monitoring Method Using One-Dimensional Convolutional Neural Network. Sensors 2022, 22, 8343. https://doi.org/10.3390/s22218343
Yang X, Yuan R, Lv Y, Li L, Song H. A Novel Multivariate Cutting Force-Based Tool Wear Monitoring Method Using One-Dimensional Convolutional Neural Network. Sensors. 2022; 22(21):8343. https://doi.org/10.3390/s22218343
Chicago/Turabian StyleYang, Xu, Rui Yuan, Yong Lv, Li Li, and Hao Song. 2022. "A Novel Multivariate Cutting Force-Based Tool Wear Monitoring Method Using One-Dimensional Convolutional Neural Network" Sensors 22, no. 21: 8343. https://doi.org/10.3390/s22218343
APA StyleYang, X., Yuan, R., Lv, Y., Li, L., & Song, H. (2022). A Novel Multivariate Cutting Force-Based Tool Wear Monitoring Method Using One-Dimensional Convolutional Neural Network. Sensors, 22(21), 8343. https://doi.org/10.3390/s22218343