Metal-based additive manufacturing condition monitoring methods: From measurement to control

doi:10.1016/j.isatra.2021.03.001

HFCAS OpenIR

	Metal-based additive manufacturing condition monitoring methods: From measurement to control
	Lin, Xin 1,3; Zhu, Kunpeng 1,2; Fuh, Jerry Ying Hsi 3; Duan, Xianyin 1
	2022
发表期刊	ISA TRANSACTIONS
ISSN	0019-0578
通讯作者	Zhu, Kunpeng(kunpengz@hotmail.com)
摘要	Compared with other additive manufacturing processes, the metal-based additive manufacturing (MAM) can build higher precision and higher density parts, and have unique advantages in the applications to automotive, medical, and aerospace industries. However, the quality defects of builds, such as dimensional accuracy, layer morphology, mechanical and metallurgical defects, have been hindering the wide applications of MAM technologies. These decrease the repeatability and consistency of build quality. In order to overcome these shortcomings and to produce high-quality parts, it is very important to carry out online monitoring and process control in the building process. A process monitoring system is demanded which can automatically optimize the process parameters to eliminate incipient defects, improve the process stability and the final build quality. In this paper, the current representative studies are selected from the literature, and the research progress of MAM process monitoring and control are surveyed. Taking the key components of the MAM monitoring system as the mainstream, this study investigates the MAM monitoring system, measurement and signal acquisition, signal and image processing, as well as machine learning methods for the process monitoring and quality classification. The advantages and disadvantages of their algorithmic implementations and applications are discussed and summarized. Finally, the prospects of MAM process monitoring researches are advised. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.
关键词	Metal-based additive manufacturing Condition monitoring Measurement and control Machine learning
DOI	10.1016/j.isatra.2021.03.001
关键词[WOS]	OPTIMIZING PROCESS PARAMETERS ; CONVOLUTIONAL NEURAL-NETWORK ; IN-SITU MEASUREMENTS ; FUSION AM PROCESS ; STAINLESS-STEEL ; MELT POOL ; ACOUSTIC-EMISSION ; DEFECT DETECTION ; FORMATION MECHANISMS ; ANOMALY DETECTION
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[51805384] ; National Natural Science Foundation of China[51875379]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Automation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS类目	Automation & Control Systems ; Engineering, Multidisciplinary ; Instruments & Instrumentation
WOS记录号	WOS:000766271300012
出版者	ELSEVIER SCIENCE INC
引用统计	被引频次：22[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/128716
专题	中国科学院合肥物质科学研究院
通讯作者	Zhu, Kunpeng
作者单位	1.Wuhan Univ Sci & Technol, Dept Mech Automat, Wuhan 430081, Peoples R China 2.Chinese Acad Sci, Inst Adv Mfg Technol, Changzhou, Peoples R China 3.Natl Univ Singapore NUS, Dept Mech Engn, Singapore, Singapore
推荐引用方式 GB/T 7714	Lin, Xin,Zhu, Kunpeng,Fuh, Jerry Ying Hsi,et al. Metal-based additive manufacturing condition monitoring methods: From measurement to control[J]. ISA TRANSACTIONS,2022,120.
APA	Lin, Xin,Zhu, Kunpeng,Fuh, Jerry Ying Hsi,&Duan, Xianyin.(2022).Metal-based additive manufacturing condition monitoring methods: From measurement to control.ISA TRANSACTIONS,120.
MLA	Lin, Xin,et al."Metal-based additive manufacturing condition monitoring methods: From measurement to control".ISA TRANSACTIONS 120(2022).