An Assembly Quality Prediction Method for Automotive Instrument Clusters Using CNN-SVR

被引：0
|
作者：
He Y. [1 ]
Xiao Z. [1 ]
Li Y. [1 ]
Wu P. [1 ]
Liu D. [2 ]
Du J. [2 ]
机构：
[1] State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing
[2] Chongqing Yazaki Meter Co., Ltd., Chongqing
来源：
Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2022年 / 33卷 / 07期
关键词：
Automotive instrument cluster; Convolutional neural network(CNN); Quality prediction; Support vector regression(SVR);
D O I：
10.3969/j.issn.1004-132X.2022.07.009
中图分类号：
学科分类号：
摘要：
Due to the long quality inspection time during assembly and lower production efficiency of automotive instrument clusters, an assembly quality prediction method for automotive instrument clusters using CNN-SVR was proposed. Combined with the assembly processes of instrument products, the production data features were extracted through CNN, which were used as the inputs of SVR to predict the pointer deflection angle that characterizesd the quality of instruments. The original production data of the instruments were obtained through the quality inspection systems of the assembly workshops, and the pointer deflection angles under different quality inspection conditions were predicted. The results indicate that proposed method has smaller prediction errors and strong generalization ability, which may accurately and effectively predict the assembly quality of automobile instrument clusters. © 2022, China Mechanical Engineering Magazine Office. All right reserved.
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页码：825 / 833
页数：8
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