Prediction of subsurface microcrack depth of brittle materials based on co-training SVR

• Published in: Jin gang shi yu mo liao mo ju gong cheng Vol. 43; no. 6; pp. 704 - 711
• Main Authors: Chuang REN, Xin SHENG, Fengli NIU, Yongwei ZHU
• Format: Journal Article
• Language: Chinese
• Published: Zhengzhou Research Institute for Abrasives & Grinding Co., Ltd 01.12.2023
• Subjects: brittle material; co-training; particle swarm optimization support vector regression(pso-svr); small sample; subsurface damage; support vector regression(svr)
• ISSN: 1006-852X
• DOI: 10.13394/j.cnki.jgszz.2023.0006

In order to overcome the dilemma of insufficient effective sample number for subsurface microcrack depth in the lapping of brittle materials with fixed abrasives and achieve accurate prediction, a co-training SVR was used to construct the prediction model. The effects of different labeled training set partitioning methods on the mean square error of the test set were compared. Then the predictive performance of supervised learning PSO-SVR model was compared with that of the model. Finally, brittle materials such as microcrystalline glass and calcium fluoride, which were not included in the labeled training set, were taken as processing objects for lapping and angular polishing experiments to examine crack depth values. The examined subsurface microcrack depths of four groups were compared with the predicted values of the co-training SVR model. The results show that the co-training SVR model under the separate partitioning method has a smaller mean square error. Compared with the PSO-SVR model, the mean square