Feature selection and domain adaptation for cross-machine product quality prediction

Today's manufacturing systems are becoming increasingly complex, dynamic and connected hence continual prediction of manufactured product quality is a key to look for patterns that can eventually lead to improved accuracy and productivity. Recent developments in artificial intelligence, especially machine learning have shown great potential to transform the manufacturing domain through analytics for processing vast amounts of manufacturing data generated (Esmaeilian et al. in J Manuf Syst 39:79-100, 2016). Although prediction models have been built to predict product quality with good accuracy, they assume that same distribution applies on training data and testing data hence fail to produce satisfying results when machines work under different conditions with varying data distribution. Naive re-collection and re-annotation of data for each new working condition can be very expensive thus is not a feasible solution. To cope with this problem, we adopt transfer learning approach called domain adaptation to transfer the knowledge learned from one labelled operating condition (source domain) to another operating condition (target domain) without labels. Particularly, we propose an end-to-end framework for cross-machine product quality prediction, which is able to alleviate domain shift problem. To facilitate the cross-machine prediction performance, a systematic feature selection approach is designed and integrated to generate most suitable feature set to characterize the collected data. Comprehensive experiments have been conducted using actual manufacturing data and the results demonstrate significant improvement on cross-machine product quality prediction as compared to conventional techniques.

Automated summary

Today's manufacturing systems are complex and dynamic. Predicting product quality is crucial for improving accuracy and productivity. Recent developments in artificial intelligence, especially machine learning, offer great potential for analyzing large amounts of manufacturing data. This study proposes an end-to-end framework using transfer learning for cross-machine product quality prediction, achieving significant improvement compared to conventional techniques.