Study on Single-point Dresser Wear Intelligent Prediction Method

Authors

Yulun Chi, University of Shanghai for Science and Technology, Shanghai 200093, China;
Haolin Li, University of Shanghai for Science and Technology, Shanghai 200093, China;
Yue Tai, University of Shanghai for Science and Technology, Shanghai 200093, China;

Abstract

Abstract: In precision grinding process, grinding wheel should be dressed in time to keep the wheel sharpness and correct geometry. Now, it is still a difficult problem how to effectively predict the dresser wear in process. According to the single-point dresser wear mechanism, a method was proposed to predict the single-point dresser wear based on the acoustic emission signal and the sequential minimal optimization support vector machines (SMO-SVM) model. The wavelet packet algorithm was used to exact acoustic emission signal characteristic information. According to the large sample of acoustic emission signal, the sequential minimal optimization support vector machines (SMO-SVM) model was established to predict single-point dresser wear, and the signal characteristic information is as input for SMO-SVM model. The experiment result shows that the model's accuracy is above 95.257 1%.

Recommended Citation

Chi, Yulun; Li, Haolin; and Tai, Yue (2020) "Study on Single-point Dresser Wear Intelligent Prediction Method," Journal of System Simulation: Vol. 29: Iss. 6, Article 7.
DOI: 10.16182/j.issn1004731x.joss.201706007
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss6/7

First Page

1210

Revised Date

2015-09-28

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201706007

Last Page

1217

CLC

TH123

Recommended Citation

Chi Yulun, Li Haolin, Yue Tai. Study on Single-point Dresser Wear Intelligent Prediction Method[J]. Journal of System Simulation, 2017, 29(6): 1210-1217.

DOI

10.16182/j.issn1004731x.joss.201706007