关键词: 多工序制造过程, 关键质量特性, 状态空间模型, Bootstrap, 改进Adaptive Lasso

Abstract: To solve the problems of multiple correlations, high data dimensions, small samples existing in the key quality characteristics identification of multistage manufacturing process, the Ada-Lasso method is improved by Principle Component Regression, integrated with the state space idea and Bootstrap method to identify the key quality characteristics in multistage process. Firstly, the State Space idea is introduced to construct the identification model of key quality characteristics in multistage process; the samples are reconstructed by Bootstrap method to expand the sample size. Then, the improved Adaptive Lasso method is adopted to identify the key quality characteristics, and the effectiveness of Adaptive Lasso, Lasso, Ridge Regression and improved Adaptive Lasso is through simulation verification under different correlation degrees between quality characteristics. Finally, the application process of improved Adaptive Lasso method is illustrated by an example. The simulation and example results show that the improved Adaptive Lasso method has a good ability to identify the key quality characteristics of multistage processes, especially when there is a strong correlation between quality characteristics, it is significantly superior to the other two methods.

Key words: multistage manufacturing process, key quality characteristics, state space model, bootstrap, improved adaptive lasso