Data fusion analysis in the powder-bed fusion AM process monitoring by Dempster-Shafer evidence theory
ISSN: 1355-2546
Article publication date: 18 November 2021
Issue publication date: 5 May 2022
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.
Keywords
Acknowledgements
The authors would like to acknowledge the supports from the China Scholarship Council for providing support and the National University of Singapore through research attachment for this work. Some of the latest work is also supported by a grant from the Basic and Applied Basic Research Programs of Guangzhou City (No.202102020680).
The authors would like to acknowledge the supports from National University of Singapore through research attachment for this work. Some of the latest work is also supported by a grant from the Basic and Applied Basic Research Programs of Guangzhou City (No. 202102020680) and a grant from National Key R&D Program of China (No. 2021YFE0203500).
Citation
Zhang, Y., Yan, W., Hong, G.S., Fuh, J.F.H., Wang, D., Lin, X. and Ye, D. (2022), "Data fusion analysis in the powder-bed fusion AM process monitoring by Dempster-Shafer evidence theory", Rapid Prototyping Journal, Vol. 28 No. 5, pp. 841-854. https://doi.org/10.1108/RPJ-10-2020-0242
Publisher
:Emerald Publishing Limited
Copyright © 2021, Emerald Publishing Limited