Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-Sensor Fusion
Abstract
:1. Introduction
2. Evaluating Grinding Wheel Wear State
3. Evaluation of Grinding Wheel Wear State
3.1. Acoustic Emission Signal Characteristic Value Extraction
- (1)
- The low-frequency coefficient and high-frequency coefficient of wavelet packet decomposition are determined using the wavelet packet energy coefficient method. The low-frequency coefficient and high-frequency coefficient of wavelet packet decomposition are determined by the wavelet packet energy coefficient method.
- (2)
- We set the threshold value of the product of the mean square value of the wavelet packet coefficients and the weighting coefficient to be less than 1 in the energy-intensive frequency band, denoted by .
- (3)
- After extracting wavelet coefficients greater than the threshold T to describe the characteristics of the signal, and are used to represent the wavelet packet coefficients after threshold processing of the high-frequency and low-frequency parts of the signal, namely
3.2. Acoustic Characteristic Value Extraction of Grinding Force and Vibration Signals
3.3. Feature Value Selection
4. Construction of Intelligent Monitoring System for Grinding Wheel Wear Monitoring
4.1. Support Vector Machine
- (1)
- Establish the classification hyperplane of the sample data and calculate the distance to the classification hyperplane as follows: .
- (2)
- Maximize the interval classification of the samples and ensure that the training samples n meet the following:
- (3)
- Solve the inner product of the high-dimensional vector into the inner product of the low-dimensional vector by applying kernel function .
- (4)
- Solve the established convex quadratic programming problem, as this will lead to the realization of target sample classification selection.
4.2. Multi-Eigenvalue Fusion Algorithm
4.3. Establishment of Grinding Wheel Wear System Based on Support Vector Machine
5. Experimental Study
5.1. The Purpose and Conditions of the Experiment
5.2. Experimental Results
6. Conclusions
- (1)
- This paper presents a multi-eigenvalue fusion algorithm based on an improved support vector machine. This algorithm overcomes the challenges of multi-sensor eigenvalue fusion, as compared to traditional BP neural networks, and significantly enhances the stability and reliability of on-line grinding wheel wear monitoring.
- (2)
- We introduced eigenvalue threshold processing and the entropy weight evaluation method to adjust the fusion strategy in order to provide a theoretical basis for the on-line monitoring of grinding wheel wear status.
- (3)
- An improved SVM was used to establish an intelligent model for grinding wheel wear monitoring, and the grinding wheel wear state was predicted through the use of this network. The experimental results show that the monitored wear state of the grinding wheel is basically consistent with the actual wear state of the grinding wheel, and the recognition rate can reach more than 92%.
- (4)
- At present, the multi-sensor fusion strategy proposed in this paper is limited to the on-line monitoring of grinding wheels in the field of precision grinding. In order to extend the application of this technology to other forms of tool wear diagnosis, its limitations will need to be addressed. However, this strategy of multi-sensor fusion can be used for equipment-based fault diagnosis.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Basic Parameters | Model |
---|---|
Machine model | SL500 grinding machine produced by Hangzhou Machine Tool Factory in China |
Wheel model | P300X40X76.2 (SIC Wheel) |
Specimen | TC4(Ti-64Al4V)(Annealed) |
Wheel speed | 25 m/s |
Experiment Serial Number | Table Feed Speed Vw (mm/min) | (mm) |
---|---|---|
1 | 3200 in X direction, 600 in Z direction | 0.001 |
2 | 3200 in X direction, 600 in Z direction | 0.003 |
3 | 3200 in X direction, 600 in Z direction | 0.005 |
Experiment Serial Number | Band 1 | Band 2 | Band 3 | |||
---|---|---|---|---|---|---|
1 | 0.347 | 474 | 0.278 | 376 | 0.378 | 411 |
0.369 | 427 | 0.254 | 354 | 0.356 | 422 | |
0.387 | 419 | 0.201 | 320 | 0.340 | 479 | |
2 | 0.354 | 465 | 0.254 | 354 | 0.398 | 463 |
0.304 | 443 | 0.265 | 346 | 0.314 | 452 | |
0.357 | 401 | 0.236 | 332 | 0.324 | 418 | |
3 | 0.342 | 398 | 0.204 | 306 | 0.207 | 387 |
0.392 | 475 | 0.245 | 369 | 0.297 | 362 | |
0.306 | 403 | 0.256 | 348 | 0.268 | 342 |
Experiment Serial Number | |||
---|---|---|---|
1 | 1.01 | 0.98 | 1.05 |
1.05 | 0.68 | 1 | |
1.1 | 1.07 | 1.03 | |
2 | 1.04 | 0.99 | 1.04 |
1.07 | 0.97 | 1 | |
1.08 | 1.09 | 1 | |
3 | 1.24 | 1.35 | 1.25 |
1.23 | 1.09 | 1.1 | |
1.17 | 1.14 | 1.24 |
Experiment Serial Number | |||
---|---|---|---|
1 | 0.71284 | 0.72486 | 0.75942 |
0.78429 | 0.71286 | 0.71287 | |
0.70354 | 0.78426 | 0.76042 | |
2 | 0.73218 | 0.77425 | 0.77041 |
0.74621 | 0.70942 | 0.73694 | |
0.69983 | 0.72618 | 0.70043 | |
3 | 1.07125 | 1.04287 | 1.04807 |
1.10758 | 1.06248 | 1.14063 | |
1.09075 | 1.12450 | 1.15407 |
Experiment | AE Feature | Force Feature σ | Vibration RMS | Output | Real | Right or Not |
---|---|---|---|---|---|---|
1 | 0.347 | 1.01 | 0.71284 | Mid-term | Mid-term | Right |
1 | 0.369 | 1.05 | 0.78429 | Mid-term | Mid-term | Right |
2 | 0.387 | 1.1 | 0.70351 | Mid-term | Mid-term | Right |
········· | ||||||
3 | 0.392 | 1.23 | 1.10758 | Late-term | Mid-term | Not |
3 | 0.306 | 1.17 | 1.09075 | Late-term | Late-term | Right |
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Duan, J.; Cao, G.; Ma, G.; Yu, Z.; Shao, C. Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-Sensor Fusion. Sensors 2024, 24, 5888. https://doi.org/10.3390/s24185888
Duan J, Cao G, Ma G, Yu Z, Shao C. Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-Sensor Fusion. Sensors. 2024; 24(18):5888. https://doi.org/10.3390/s24185888
Chicago/Turabian StyleDuan, Jingsong, Guohua Cao, Guoqing Ma, Zhenglin Yu, and Changshun Shao. 2024. "Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-Sensor Fusion" Sensors 24, no. 18: 5888. https://doi.org/10.3390/s24185888