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

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Feature selection and domain adaptation for cross-machine product quality prediction
Title:
Feature selection and domain adaptation for cross-machine product quality prediction
Journal Title:
Journal of Intelligent Manufacturing
Publication Date:
23 November 2021
Citation:
Wang, Y., Cui, W., Vuong, N. K., Chen, Z., Zhou, Y., & Wu, M. (2021). Feature selection and domain adaptation for cross-machine product quality prediction. Journal of Intelligent Manufacturing. doi:10.1007/s10845-021-01875-z
Abstract:
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. 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. Naïve 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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - IAF-PP - Advanced Manufacturing & Engineering
Grant Reference no. : A1723a0035
Description:
This is a post-peer-review, pre-copyedit version of an article published in Journal of Intelligent Manufacturing. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10845-021-01875-z
ISSN:
0956-5515
1572-8145
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