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

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
Main Authors: Chuang REN, Xin SHENG, Fengli NIU, Yongwei ZHU
Format: Article
Language:zho
Published: Zhengzhou Research Institute for Abrasives & Grinding Co., Ltd. 2023-12-01
Series:Jin'gangshi yu moliao moju gongcheng
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

Internet

http://www.jgszz.cn/article/doi/10.13394/j.cnki.jgszz.2023.0006

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