3D printing in materials manufacturing industry: A realm of Industry 4.0

被引:16
|
作者
Tamir, Tariku Sinshaw [1 ,2 ,3 ]
Xiong, Gang [2 ,4 ]
Shen, Zhen [2 ,5 ]
Leng, Jiewu [1 ]
Fang, Qihang [2 ]
Yang, Yong [6 ]
Jiang, Jingchao [7 ]
Lodhi, Ehtisham [2 ]
Wang, Fei-Yue [2 ,4 ]
机构
[1] Guangdong Univ Technol, State Key Lab Precis Elect Mfg Technol & Equipment, Guangzhou 510006, Peoples R China
[2] Chinese Acad Sci, Inst Automat, Beijing Engn Res Ctr Intelligent Syst & Technol, Beijing 100190, Peoples R China
[3] Debremarkos Univ, Inst Technol, Debremarkos 269, Ethiopia
[4] Chinese Acad Sci, Guangdong Engn Res Ctr Printing & Intelligent Mfg, Cloud Comp Ctr, Dongguan 523808, Peoples R China
[5] Qingdao Acad Intelligent Ind, Intelligent Mfg Ctr, Qingdao 266109, Peoples R China
[6] Chinese Acad Sci, Shanghai Inst Ceram, State Key Lab High Performance Ceram & Superfine M, Shanghai 201899, Peoples R China
[7] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Shatin, Hong Kong, Peoples R China
基金
中国国家自然科学基金;
关键词
3D printing; Additive manufacturing; Closed-loop control; Machine learning; Industry; 4.0; OF-THE-ART; DIGITAL TWIN; FEEDBACK-CONTROL; QUALITY-CONTROL; PATH GENERATION; LASER; DEPOSITION; PREDICTION; FUTURE; OPTIMIZATION;
D O I
10.1016/j.heliyon.2023.e19689
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Additive manufacturing (AM), also known as 3D printing, is a new manufacturing trend showing promising progress over time in the era of Industry 4.0. So far, various research has been done for increasing the reliability and productivity of a 3D printing process. In this regard, reviewing the existing concepts and forwarding novel research directions are important. This paper reviews and summarizes the process flow, technologies, configurations, and monitoring of AM. It started with the general AM process flow, followed by the definitions and the working principles of various AM technologies and the possible AM configurations, such as traditional and robot-assisted AM. Then, defect detection, fault diagnosis, and open-loop and closed-loop control systems in AM are discussed. It is noted that introducing robots into the assisting mechanism of AM increases the reliability and productivity of the manufacturing process. Moreover, integrating machine learning and conventional control algorithms ensures a closed-loop control in AM, a promising control strategy. Lastly, the paper addresses the challenges and future trends.
引用
收藏
页数:20
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