TY - JOUR
T1 - Machine-learning-based monitoring and optimization of processing parameters in 3D printing
AU - Tamir, Tariku Sinshaw
AU - Xiong, Gang
AU - Fang, Qihang
AU - Yang, Yong
AU - Shen, Zhen
AU - Zhou, Meng Chu
AU - Jiang, Jingchao
N1 - Funding Information:
This work was supported in part by the National Key Research and Development Program of China (No. 2018YFB1700403); National Natural Science Foundation of China under Grants U1909204, U1909218, U1811463, 61872365 & 61806198; CAS Key Technology Talent Program (Zhen Shen); The Guangdong Basic and Applied Basic Research Foundation under Grant 2021B1515140034; The Foshan Science and Technology Innovation Team Project under Grant 2018IT100142; The Scientific Instrument Developing Project of the Chinese Academy of Sciences under Grant No. YZQT014; CAS STS Dongguan Joint Project 20201600200072
Publisher Copyright:
© 2022 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - 3D printing
KW - Additive manufacturing
KW - closed-loop
KW - digital manufacturing
KW - machine learning
KW - processing parameters
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U2 - 10.1080/0951192X.2022.2145019
DO - 10.1080/0951192X.2022.2145019
M3 - Article
AN - SCOPUS:85142274532
SN - 0951-192X
VL - 36
SP - 1362
EP - 1378
JO - International Journal of Computer Integrated Manufacturing
JF - International Journal of Computer Integrated Manufacturing
IS - 9
ER -