Machine-learning-based monitoring and optimization of processing parameters in 3D printing

doi:10.1080/0951192X.2022.2145019

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Machine-learning-based monitoring and optimization of processing parameters in 3D printing
	Tamir, Tariku Sinshaw 1,2; Xiong, Gang1,3 ; Fang, Qihang 1,2; Yang, Yong4 ; Shen, Zhen1,3 ; Zhou, MengChu 5,6,7; Jiang, Jingchao 8
发表期刊	INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
ISSN	0951-192X
	2022-11-19
页码	17
通讯作者	Shen, Zhen(zhen.shen@ia.ac.cn)
摘要	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.
关键词	Additive manufacturing closed-loop 3D printing digital manufacturing machine learning processing parameters
DOI	10.1080/0951192X.2022.2145019
关键词[WOS]	MANUFACTURING METHODS ; PREDICTION ; LIQUID ; FUTURE
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFB1700403] ; National Natural Science Foundation of China[U1909204] ; National Natural Science Foundation of China[U1909218] ; National Natural Science Foundation of China[U1811463] ; National Natural Science Foundation of China[61872365] ; National Natural Science Foundation of China[61806198] ; CAS Key Technology Talent Program (Zhen Shen) ; Guangdong Basic and Applied Basic Research Foundation[2021B1515140034] ; Foshan Science and Technology Innovation Team Project[2018IT100142] ; Scientific Instrument Developing Project of the Chinese Academy of Sciences[YZQT014] ; CAS STS Dongguan Joint Project[20201600200072]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; CAS Key Technology Talent Program (Zhen Shen) ; Guangdong Basic and Applied Basic Research Foundation ; Foshan Science and Technology Innovation Team Project ; Scientific Instrument Developing Project of the Chinese Academy of Sciences ; CAS STS Dongguan Joint Project
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Manufacturing ; Operations Research & Management Science
WOS记录号	WOS:000889012900001
出版者	TAYLOR & FRANCIS LTD
引用统计	被引频次：19[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50789
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Shen, Zhen
作者单位	1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Beijing Engn Res Ctr Intelligent Syst & Technol, Inst Automat, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 3.Chinese Acad Sci, Guangdong Engn Res Ctr 3D Printing & Intelligent, Cloud Comp Ctr, Dongguan, Peoples R China 4.Chinese Acad Sci, Shanghai Inst Ceram, State Key Lab High Performance Ceram & Superfine, Shanghai, Peoples R China 5.New Jersey Inst Technol, Helen & John C Hartmann Dept Elect & Comp Engn, Newark, NJ 07102 USA 6.Macau Univ Sci & Technol, Macao Inst Syst Engn, Macau 999078, Peoples R China 7.Macau Univ Sci & Technol, Collaborat Lab Intelligent Sci & Syst, Macau 999078, Peoples R China 8.Chinese Univ Hong Kong, Dept Mech & Automat Engn, Shatin, Hong Kong, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Tamir, Tariku Sinshaw,Xiong, Gang,Fang, Qihang,et al. Machine-learning-based monitoring and optimization of processing parameters in 3D printing[J]. INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING,2022:17.
APA	Tamir, Tariku Sinshaw.,Xiong, Gang.,Fang, Qihang.,Yang, Yong.,Shen, Zhen.,...&Jiang, Jingchao.(2022).Machine-learning-based monitoring and optimization of processing parameters in 3D printing.INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING,17.
MLA	Tamir, Tariku Sinshaw,et al."Machine-learning-based monitoring and optimization of processing parameters in 3D printing".INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING (2022):17.