Metal-based additive manufacturing condition monitoring methods: From measurement to control
文献类型：期刊论文
| 作者 | Lin, Xin1,3; Zhu, Kunpeng2,3; Fuh, Jerry Ying Hsi1; Duan, Xianyin3 |
| 刊名 | ISA TRANSACTIONS
 |
| 出版日期 | 2022 |
| 卷号 | 120 |
| 关键词 | Metal-based additive manufacturing Condition monitoring Measurement and control Machine learning |
| ISSN号 | 0019-0578 |
| DOI | 10.1016/j.isatra.2021.03.001 |
| 通讯作者 | Zhu, Kunpeng(kunpengz@hotmail.com) |
| 英文摘要 | 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. |
| WOS关键词 | OPTIMIZING PROCESS PARAMETERS ; CONVOLUTIONAL NEURAL-NETWORK ; IN-SITU MEASUREMENTS ; FUSION AM PROCESS ; STAINLESS-STEEL ; MELT POOL ; ACOUSTIC-EMISSION ; DEFECT DETECTION ; FORMATION MECHANISMS ; ANOMALY DETECTION |
| 资助项目 | National Natural Science Foundation of China[51805384] ; National Natural Science Foundation of China[51875379] |
| WOS研究方向 | Automation & Control Systems ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000766271300012 |
| 出版者 | ELSEVIER SCIENCE INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/128715]  |
| 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.Natl Univ Singapore NUS, Dept Mech Engn, Singapore, Singapore 2.Chinese Acad Sci, Inst Adv Mfg Technol, Changzhou, Peoples R China 3.Wuhan Univ Sci & Technol, Dept Mech Automat, Wuhan 430081, Peoples R China |
| 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). |
入库方式： OAI收割
来源：合肥物质科学研究院
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