基于协同训练SVR的脆性材料亚表面微裂纹深度预测
TG58%TG732; 为克服固结磨料研磨脆性材料的亚表面微裂纹深度有效样本数不足的困境,实现其准确预测,采用协同训练SVR构建预测模型,对比不同标记训练集划分方法对测试集均方误差的影响;后以监督学习PSO-SVR模型为对照,比较二者的预测性能;最后以标记训练集未包含的脆性材料微晶玻璃和氟化钙为加工对象,进行工件的研磨及角度抛光法裂纹深度检测实验,并将检测的4组亚表面微裂纹深度值与协同训练SVR模型的预测值对比.结果表明:分开划分法下的协同训练SVR模型具有更小的均方误差;相比于PSO-SVR模型,协同训练SVR模型的均方误差和平均绝对百分比误差分别减小9％和17％,且其对4组验证实验的预测误...
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| Published in: | 金刚石与磨料磨具工程 Vol. 43; no. 6; pp. 704 - 711 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | Chinese |
| Published: |
南京航空航天大学机电学院,南京 210016
01.12.2023
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| Subjects: | |
| ISSN: | 1006-852X |
| Online Access: | Get full text |
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| Abstract | TG58%TG732; 为克服固结磨料研磨脆性材料的亚表面微裂纹深度有效样本数不足的困境,实现其准确预测,采用协同训练SVR构建预测模型,对比不同标记训练集划分方法对测试集均方误差的影响;后以监督学习PSO-SVR模型为对照,比较二者的预测性能;最后以标记训练集未包含的脆性材料微晶玻璃和氟化钙为加工对象,进行工件的研磨及角度抛光法裂纹深度检测实验,并将检测的4组亚表面微裂纹深度值与协同训练SVR模型的预测值对比.结果表明:分开划分法下的协同训练SVR模型具有更小的均方误差;相比于PSO-SVR模型,协同训练SVR模型的均方误差和平均绝对百分比误差分别减小9％和17％,且其对4组验证实验的预测误差在1.2％～13.8％.表明协同训练SVR模型,可较为准确地预测固结磨料研磨脆性材料的亚表面微裂纹深度. |
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| AbstractList | TG58%TG732; 为克服固结磨料研磨脆性材料的亚表面微裂纹深度有效样本数不足的困境,实现其准确预测,采用协同训练SVR构建预测模型,对比不同标记训练集划分方法对测试集均方误差的影响;后以监督学习PSO-SVR模型为对照,比较二者的预测性能;最后以标记训练集未包含的脆性材料微晶玻璃和氟化钙为加工对象,进行工件的研磨及角度抛光法裂纹深度检测实验,并将检测的4组亚表面微裂纹深度值与协同训练SVR模型的预测值对比.结果表明:分开划分法下的协同训练SVR模型具有更小的均方误差;相比于PSO-SVR模型,协同训练SVR模型的均方误差和平均绝对百分比误差分别减小9％和17％,且其对4组验证实验的预测误差在1.2％～13.8％.表明协同训练SVR模型,可较为准确地预测固结磨料研磨脆性材料的亚表面微裂纹深度. |
| Abstract_FL | 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 er-ror of the test set were compared.Then the predictive performance of supervised learning PSO-SVR model was com-pared 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 pre-dicted values of the co-training SVR model.The results show that the co-training SVR model under the separate parti-tioning method has a smaller mean square error.Compared with the PSO-SVR model,the mean square error and the mean absolute percentage error of the co-training SVR model are reduced by 9％and 17％,respectively.The prediction error of the model for the four groups of verification experiments is between 1.2％and 13.8％.The above results show that the co-training SVR model can predict the subsurface microcrack depth accurately when lapping brittle materials with fixed abrasives. |
| Author | 朱永伟 牛凤丽 任闯 盛鑫 |
| AuthorAffiliation | 南京航空航天大学机电学院,南京 210016 |
| AuthorAffiliation_xml | – name: 南京航空航天大学机电学院,南京 210016 |
| Author_FL | SHENG Xin ZHU Yongwei NIU Fengli REN Chuang |
| Author_FL_xml | – sequence: 1 fullname: REN Chuang – sequence: 2 fullname: SHENG Xin – sequence: 3 fullname: NIU Fengli – sequence: 4 fullname: ZHU Yongwei |
| Author_xml | – sequence: 1 fullname: 任闯 – sequence: 2 fullname: 盛鑫 – sequence: 3 fullname: 牛凤丽 – sequence: 4 fullname: 朱永伟 |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DOI | 10.13394/j.cnki.jgszz.2023.0006 |
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| DocumentTitle_FL | Prediction of subsurface microcrack depth of brittle materials based on co-training SVR |
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| Keywords | 亚表面损伤 支持向量回归 小样本 脆性材料 粒子群优化支持向量回归 协同训练 co-training brittle material support vector regression(SVR) small sample subsurface damage particle swarm optimization support vector regression(PSO-SVR) |
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| PublicationTitle | 金刚石与磨料磨具工程 |
| PublicationTitle_FL | Diamond & Abrasives Engineering |
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| Publisher | 南京航空航天大学机电学院,南京 210016 |
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| Snippet | TG58%TG732; 为克服固结磨料研磨脆性材料的亚表面微裂纹深度有效样本数不足的困境,实现其准确预测,采用协同训练SVR构建预测模型,对比不同标记训练集划分方法对测试集均方... |
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| Title | 基于协同训练SVR的脆性材料亚表面微裂纹深度预测 |
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