An integrated manifold learning approach for high-dimensional data feature extractions and its applications to online process monitoring of additive manufacturing. (2nd November 2021)

• Record Type: Journal Article
• Authors: Liu, Chenang; Kong, Zhenyu (James); Babu, Suresh; Joslin, Chase; Ferguson, James
• Is Part Of: IISE transactions. Volume 53:Number 11(2021)
• Page Start: 1215
• Page End: 1230
• Publication Date: 2021-11-02
• Subjects: Additive manufacturing -- integrated manifold learning -- isometric feature mapping (ISOMAP) -- multi-kernel metric learning (MKML) -- online process monitoring; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: Https://www.tandfonline.com/doi/10.1080/24725854.2020.1849876
• Languages: English
• ISSNs: 2472-5854

Abstract

Abstract: As an effective dimension reduction and feature extraction technique, manifold learning has been successfully applied to high-dimensional data analysis. With the rapid development of sensor technology, a large amount of high-dimensional data such as image streams can be easily available. Thus, a promising application of manifold learning is in the field of sensor signal analysis, particular for the applications of online process monitoring and control using high-dimensional data. The objective of this study is to develop a manifold learning-based feature extraction method for process monitoring of Additive Manufacturing (AM) using online sensor data. Due to the non-parametric nature of most existing manifold learning methods, their performance in terms of computational efficiency, as well as noise resistance has yet to be improved. To address this issue, this study proposes an integrated manifold learning approach termed multi-kernel metric learning embedded isometric feature mapping (MKML-ISOMAP) for dimension reduction and feature extraction of online high-dimensional sensor data such as images. Based on the extracted features with the utilization of supervised classification and regression methods, an online process monitoring methodology for AM is implemented to identify the actual process quality status. In the numerical simulation and real-world case studies, the proposed method demonstrates excellent performance in both prediction accuracy and computational efficiency.