期刊
JOURNAL OF TRIBOLOGY-TRANSACTIONS OF THE ASME
卷 144, 期 9, 页码 -出版社
ASME
DOI: 10.1115/1.4054234
关键词
ultrasonic grinding; grinding parameters; surface roughness; characterization; inversion
资金
- National Key R&D Program of China [2018YFB2001300]
- National Science and Technology Major Project [2017-VII-0003-0096]
- National Natural Science Foundation of China (NSFC) [51705142]
This research proposes a combination of statistical theory and data-driven analysis to solve the problems of grinding parameter inversion and surface roughness characterization. The results show that skewness and kurtosis are important for the optimal inversion model and surface characterization.
With surface roughness restricted by grinding parameters, the characterization of roughness parameters and the inversion of grinding parameters are of great significance for improving surface performance and realizing active surface machining. This research proposes a combination of statistical theory and data-driven analysis to solve the above problems. Pearson correlation analysis and multivariate variance analysis indicate the correlation characterization parameter set (CPS) consists of Sa, Vmp, Vvv, and Sz and that there are differences in the influence of grinding parameters on the parameters in CPS. Adjustment of support vector machine (SVM) core parameters makes it possible to construct expansion parameter set (EPS) optimal inversion models. By designing pseudo-surface random roughness parameters and grinding experiments, the reliability of inversion models is verified. The results show: (1) The better generalization of inversion model indicates skewness Ssk and kurtosis Sku in EPS have important implications for the optimal inversion model and surface characterization and (2) The data-driven model based on support vector machine provides machining guidance for obtaining the expected ultrasonic grinding surface.
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