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

doi:10.1080/0951192X.2022.2145019

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	Machine-learning-based monitoring and optimization of processing parameters in 3D printing
	Tamir, Tariku Sinshaw 7,8; Xiong, Gang 6,8; Fang, Qihang 7,8; Yang, Yong 5; Shen, Zhen 6,8; Zhou, MengChu 2,3,4; Jiang, Jingchao 1
刊名	INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
	2022-11-19
页码	17
关键词	Additive manufacturing closed-loop 3D printing digital manufacturing machine learning processing parameters
ISSN号	0951-192X
DOI	10.1080/0951192X.2022.2145019
通讯作者	Shen, Zhen(zhen.shen@ia.ac.cn)
英文摘要	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.
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]
WOS关键词	MANUFACTURING METHODS ; PREDICTION ; LIQUID ; FUTURE
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000889012900001
资助机构	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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50788]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Shen, Zhen
作者单位	1.Chinese Univ Hong Kong, Dept Mech & Automat Engn, Shatin, Hong Kong, Peoples R China 2.Macau Univ Sci & Technol, Collaborat Lab Intelligent Sci & Syst, Macau 999078, Peoples R China 3.Macau Univ Sci & Technol, Macao Inst Syst Engn, Macau 999078, Peoples R China 4.New Jersey Inst Technol, Helen & John C Hartmann Dept Elect & Comp Engn, Newark, NJ 07102 USA 5.Chinese Acad Sci, Shanghai Inst Ceram, State Key Lab High Performance Ceram & Superfine, Shanghai, Peoples R China 6.Chinese Acad Sci, Guangdong Engn Res Ctr 3D Printing & Intelligent, Cloud Comp Ctr, Dongguan, Peoples R China 7.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 8.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Beijing Engn Res Ctr Intelligent Syst & Technol, Inst Automat, Beijing, Peoples R China
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.