Please use this identifier to cite or link to this item: https://doi.org/10.1108/RPJ-10-2020-0242
Title: Data fusion analysis in the powder-bed fusion AM process monitoring by Dempster-Shafer evidence theory
Authors: Zhang, Y
Yan, W 
Hong, GS 
Fuh, JFH 
Wang, D
Lin, X
Ye, D
Keywords: Additive manufacturing
Condition monitoring
Data fusion
Machine learning
Dempster-Shafer evidence
Issue Date: 1-Jan-2021
Publisher: Emerald
Citation: Zhang, Y, Yan, W, Hong, GS, Fuh, JFH, Wang, D, Lin, X, Ye, D (2021-01-01). Data fusion analysis in the powder-bed fusion AM process monitoring by Dempster-Shafer evidence theory. Rapid Prototyping Journal. ScholarBank@NUS Repository. https://doi.org/10.1108/RPJ-10-2020-0242
Abstract: Purpose: This study aims to develop a data fusion method for powder-bed fusion (PBF) process monitoring based on process image information. The data fusion method can help improve process condition identification performance, which can provide guidance for further PBF process monitoring and control system development. Design/methodology/approach: Design of reliable process monitoring systems is an essential approach to solve PBF built quality. A data fusion framework based on support vector machine (SVM), convolutional neural network (CNN) and Dempster-Shafer (D-S) evidence theory are proposed in the study. The process images which include the information of melt pool, plume and spatters were acquired by a high-speed camera. The features were extracted based on an appropriate image processing method. The three feature vectors corresponding to the three objects, respectively, were used as the inputs of SVM classifiers for process condition identification. Moreover, raw images were also used as the input of a CNN classifier for process condition identification. Then, the information fusion of the three SVM classifiers and the CNN classifier by an improved D-S evidence theory was studied. Findings: The results demonstrate that the sensitivity of information sources is different for different condition identification. The feature fusion based on D-S evidence theory can improve the classification performance, with feature fusion and classifier fusion, the accuracy of condition identification is improved more than 20%. Originality/value: An improved D-S evidence theory is proposed for PBF process data fusion monitoring, which is promising for the development of reliable PBF process monitoring systems.
Source Title: Rapid Prototyping Journal
URI: https://scholarbank.nus.edu.sg/handle/10635/219556
ISSN: 1355-2546
DOI: 10.1108/RPJ-10-2020-0242
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