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

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 s...

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Bibliographic Details

Published in:	Jin'gangshi yu moliao moju gongcheng
Main Authors:	Chuang REN, Xin SHENG, Fengli NIU, Yongwei ZHU
Format:	Article
Language:	Chinese
Published:	Zhengzhou Research Institute for Abrasives & Grinding Co., Ltd. 2023-12-01
Subjects:	brittle material subsurface damage support vector regression(svr) small sample co-training particle swarm optimization support vector regression(pso-svr)
Online Access:	http://www.jgszz.cn/article/doi/10.13394/j.cnki.jgszz.2023.0006