LASSO-BN for Selection and Optimization of Product Critical Quality Features

Jiali Cheng, Zhiqiang Cai, Chen Shen, Ting Wang

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

The prediction of complex product quality has been extensively studied in the last decades. However, due to the high dimensionality and diversity of quality features, the control optimization of feature parameters remains a significant challenge. For complex products, fault detection techniques are required to accurately predict product quality, and significant influencing quality features must also be identified for their control optimization. In this paper, we propose a novel approach combining the Least Absolute Shrinkage and Selection Operator (LASSO) method with Bayesian Networks (BN) for the detection, identification and control of product quality metrics. Specifically, for complex products with high-dimensional features, the identification of key quality features is achieved initially by the LASSO method to obtain more accurate quality prediction. Subsequently, the optimal production range is determined through the utilization of a Bayesian network to achieve the optimization of product quality. The experimental results show that processing fewer but critical features can not only obtain satisfactory prediction accuracy, but also save computational time. Furthermore, this method offers practical operational guidance for product quality prediction and control in complex product industries.

源语言英语
主期刊名IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2024
出版商IEEE Computer Society
521-525
页数5
ISBN(电子版)9798350386097
DOI
出版状态已出版 - 2024
活动2024 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2024 - Bangkok, 泰国
期限: 15 12月 202418 12月 2024

出版系列

姓名IEEE International Conference on Industrial Engineering and Engineering Management
ISSN(印刷版)2157-3611
ISSN(电子版)2157-362X

会议

会议2024 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2024
国家/地区泰国
Bangkok
时期15/12/2418/12/24

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