Machine-learning-based monitoring and optimization of processing parameters in 3D printing
Document Type
Article
Publication Date
1-1-2023
Abstract
Additive manufacturing (AM), commonly known as 3D printing, is a rapidly growing technology. Guaranteeing the quality and mechanical strength of printed parts is an active research area. Most of the existing methods adopt open-loop-like Machine Learning (ML) algorithms that can be used only for predicting properties of printed parts without any quality assuring mechanism. Some closed-loop approaches, on the other hand, consider a single adjustable processing parameter to monitor the properties of a printed part. This work proposes both open-loop and closed-loop ML models and integrates them to monitor the effects of processing parameters on the quality of printed parts. By using experimental 3D printing data, an open-loop classification model formulates the relationship between processing parameters and printed part properties. Then, a closed-loop control algorithm that combines open-loop ML models and a fuzzy inference system is constructed to generate optimized processing parameters for better printed part properties. The proposed system realizes the application of a closed-loop control system to AM.
Identifier
85142274532 (Scopus)
Publication Title
International Journal of Computer Integrated Manufacturing
External Full Text Location
https://doi.org/10.1080/0951192X.2022.2145019
e-ISSN
13623052
ISSN
0951192X
First Page
1362
Last Page
1378
Issue
9
Volume
36
Grant
2018IT100142
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Tamir, Tariku Sinshaw; Xiong, Gang; Fang, Qihang; Yang, Yong; Shen, Zhen; Zhou, Meng Chu; and Jiang, Jingchao, "Machine-learning-based monitoring and optimization of processing parameters in 3D printing" (2023). Faculty Publications. 2376.
https://digitalcommons.njit.edu/fac_pubs/2376
