doi:10.16186/j.cnki.1673-9787.2020110024

Received:2020/11/06

Revised:2021/01/08

Published:2021/11/15

Surface roughness prediction of Al₂O₃ ceramics machined by ultrasonic assisted grinding based on IGWO-SVM

ZHAO Mingli, SONG Shijie, LI Bohan

School of Mechanical and Power Engineering,Henan Polytechnic University,Jiaozuo 454000 ,Henan,China

Abstract:In this work ,the ultrasonic-assisted grinding of Al₂O₃ ceramics was experimented to predict the sur-face roughness of ultrasonic-assisted grinding of Al₂O₃ ceramics. The improved gray wolf optimization algorithm（IGWO）was used to optimize the parameters of the support vector machine（SVM） ,and the IGWO-SVM pre-diction model was established to predict the surface roughness. The IGWO-SVM model was compared with PSO-SVM prediction model ,CS-SVM prediction model ,and GWO-SVM prediction model. 'The results showed that the maximum absolute error between the predicted value of the IGWO-SVM prediction model and the experimental value was 0.411 9 ,the absolute minimum error was 0.002 4，the absolute average error was 0.145 6，the square correlation coefficient was 0.931 092 ,and the mean square error was 0.000 399 8.Compared with the PSo-SVM prediction model ,the CS-SVM prediction model and CWO-SVM prediction model ,the proposed model featured higher prediction accuracy and reliability ,and surface roughness of ultrasonic-assisted grinding could be predicted more accurately.

Key words:ultrasonic machining;AlO ceramic;surface roughness;support vector machine

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