Feature selection for manufacturing process monitoring using cross-validation

A novel algorithm is developed for feature selection and parameter tuning in quality monitoring of manufacturing processes using cross-validation. Due to the recent development in sensing technology, many on-line signals are collected for manufacturing process monitoring and feature extraction is then performed to extract critical features related to product/process quality. However, lack of precise process knowledge may result in many irrelevant or redundant features. Therefore, a systematic procedure is needed to select a parsimonious set of features which provide sufficient information for process monitoring. In this study, a new method for selecting features and tuning SPC limits is proposed by applying k-fold cross-validation to simultaneously select important features and set the monitoring limits using Type I and Type II errors obtained from cross-validation. The monitoring performance for production data collected from ultrasonic metal welding of batteries demonstrates that the proposed algorithm is able to select the most efficient features and control limits and thus leading to satisfactory monitoring performance.