| ZHAO Shudong,YU Xiaomin,WANG Wenxi,ZOU Lai.GA-BP Identification of Acoustic Signals for Wear States of Pyramidal Abrasive Belts[J],53(3):28-38 |
| GA-BP Identification of Acoustic Signals for Wear States of Pyramidal Abrasive Belts |
| Received:October 27, 2023 Revised:January 18, 2024 |
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| DOI：10.16490/j.cnki.issn.1001-3660.2024.03.003 |
| KeyWord:robotic abrasive belt grinding acoustic signal Archard model genetic algorithm optimized BP neural network |
| Author | Institution |
| ZHAO Shudong |
College of Mechanical and Vehicle Engineering,Chongqing , China |
| YU Xiaomin |
AECC Aero Science and Technology Co., Ltd., Chengdu , China |
| WANG Wenxi |
College of Mechanical and Vehicle Engineering,Chongqing , China ;State Key Laboratory of Mechanical Transmissions for Advanced Equipment, Chongqing University, Chongqing , China |
| ZOU Lai |
College of Mechanical and Vehicle Engineering,Chongqing , China ;State Key Laboratory of Mechanical Transmissions for Advanced Equipment, Chongqing University, Chongqing , China |
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| Abstract: |
| Continuous wear of pyramid belts causes problems such as blunt peaks, poor material removal ability and high heat generation, which is particularly obvious in the grinding and polishing of materials such as high temperature alloys and titanium alloys. In order to avoid the phenomena of continuous reduction of processing efficiency and gradual deterioration of workpiece surface quality caused by belt wear, the prediction capability of pyramid belt wear needs to be improved. Experiments were conducted by a robotic belt grinding system and a brand new 237AA pyramid belt manufactured by 3M. The full-life pyramid belt wear experiments were conducted on titanium alloy workpieces at three different grinding speed in a dry grinding condition. The grinding sound of the abrasive belts was captured by a microphone at a transverse position located 4 cm from the grinding surface. Based on the mathematical derivation of Archard model, it was proposed to quantify the wear degree of pyramid abrasive belts in terms of Rat, and a pyramid abrasive belt wear model was obtained. Then, the frequency distribution and amplitude change of idling sound and grinding sound of abrasive belt in different wear periods were obtained by short-time Fourier to analyze the sound signals. The frequency bands with correlation with the degree of wear of abrasive belt were obtained by decomposing the wavelet packet of the original signals and extracting the features. Finally, a GA-BP model was established based on the sound signal features to predict the wear state of the pyramid abrasive belt. Kr and R0 were obtained by fitting the wear Rat. Kr was related to the characteristic parameters of the pyramid belt and characterized the wear rate of the belt cone. The difference of Kr under different speed was small, but it increased slightly with the increase of speed. R0, as the initial Rat of pyramid abrasive belts, also showed a similar law with Kr. By performing short-time Fourier analysis and wavelet packet decomposition on the grinding sound, it could be obtained that the frequency of the idling sound was mainly concentrated in the low frequency band. The grinding sound in different wear periods of abrasive belts was distributed in all frequency bands, and the sound in the low-frequency band had a similarity with the frequency distribution of the idling sound of abrasive belts. The sound characteristics of the DD2 frequency band gradually decreased with the grinding time, which was more regular than the other frequency bands. The results showed that the coefficient of determination (R2) was greater than 0.8, the mean absolute error (MAE) was less than 0.04, the mean deviation error (MBE) was in the range of ±0.002, and the mean square error (RMSE) was less than 0.05. Rat correlates extremely well with the material removal capacity of pyramid belts and accurately quantifies the degree of belt wear. As the abrasive belt wears, the sharp pyramidal cones begin to flatten out, the localized pressure of a single cone gradually decreases, the material removal capability weakens, the micro-oscillations generated by the abrasive belt to remove material become weaker and weaker, and the acoustic signature of the high-frequency signal gradually decreases. Acquisition of sound signal characteristics in the DD2 band establishes a GA-BP model to predict the wear state of the pyramid sand belt with accuracy and stability. |
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