Metal-Based Additive Manufacturing Condition Monitoring: A Review on Machine Learning Based Approaches

doi:10.1109/TMECH.2021.3110818

	Metal-Based Additive Manufacturing Condition Monitoring: A Review on Machine Learning Based Approaches
	Zhu, Kunpeng 3,4; Fuh, Jerry Ying Hsi 1,2; Lin, Xin 4
刊名	IEEE-ASME TRANSACTIONS ON MECHATRONICS
	2021-10-19
关键词	Feature extraction Process monitoring Three-dimensional printing Powders Condition monitoring Buildings Metals Condition monitoring machine learning metal-based additive manufacturing (MAM)
ISSN号	1083-4435
DOI	10.1109/TMECH.2021.3110818
通讯作者	Lin, Xin(xinlin@wust.edu.cn)
英文摘要	The metal-based additive manufacturing (MAM) processes have great potential in wide industrial applications, for their capabilities in building dense metal parts with complex geometry and internal characteristics. However, various defects in the MAM process greatly affect the precision, mechanical properties and repeatability of final parts. These defects limit its application as a reliable manufacturing process, especially in the aerospace and medical industries where high quality and reliability are essential. MAM process monitoring provides a technical basis for avoiding and eliminating defects to improve the build quality. Based on of the nature of the MAM build defects, this article conducts a thorough investigation of monitoring methods, and proposes a machine learning (ML) framework for process condition monitoring. According to the structure of ML models, they are divided into shallow ML-based and deep learning-based methods. The state-of-the-art ML monitoring approaches, as well as the advantages and disadvantages of their algorithmic implementations, are discussed. Finally, the prospects of ML based process monitoring researches are summarized and advised.
资助项目	Chinese National Key Research and Development Project[2018YFB1703200] ; Chinese Ministry of Science and Technology ; Natural Science Foundation of China[51805384] ; Natural Science Foundation of China[51875379]
WOS关键词	CONVOLUTIONAL NEURAL-NETWORK ; OPTIMIZING PROCESS PARAMETERS ; IN-SITU MEASUREMENTS ; FUSION AM PROCESS ; ACOUSTIC-EMISSION ; DEFECT DETECTION ; STAINLESS-STEEL ; MELT POOL ; DENSITY PREDICTION ; ANOMALY DETECTION
WOS研究方向	Automation & Control Systems ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000732682300001
资助机构	Chinese National Key Research and Development Project ; Chinese Ministry of Science and Technology ; Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/126942]
专题	中国科学院合肥物质科学研究院
通讯作者	Lin, Xin
作者单位	1.Natl Univ Singapore, Dept Mech Engn, Singapore 119077, Singapore 2.Natl Univ Singapore Suzhou, Res Inst, Suzhou Ind Pk, Suzhou 215128, Peoples R China 3.Chinese Acad Sci, Inst Adv Mfg Technol, Hefei Inst Phys Sci, Changzhou 213164, Peoples R China 4.Wuhan Univ Sci & Technol, Sch Machinery & Automat, Wuhan 430081, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Kunpeng,Fuh, Jerry Ying Hsi,Lin, Xin. Metal-Based Additive Manufacturing Condition Monitoring: A Review on Machine Learning Based Approaches[J]. IEEE-ASME TRANSACTIONS ON MECHATRONICS,2021.
APA	Zhu, Kunpeng,Fuh, Jerry Ying Hsi,&Lin, Xin.(2021).Metal-Based Additive Manufacturing Condition Monitoring: A Review on Machine Learning Based Approaches.IEEE-ASME TRANSACTIONS ON MECHATRONICS.
MLA	Zhu, Kunpeng,et al."Metal-Based Additive Manufacturing Condition Monitoring: A Review on Machine Learning Based Approaches".IEEE-ASME TRANSACTIONS ON MECHATRONICS (2021).