Metal based additive manufacturing condition monitoring methods: from measurement to control
Metal based additive manufacturing condition monitoring methods: from measurement to control
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金属基增材制造状态监测方法：从测量到控制
DOI：
10.1016/j.isatra.2021.03.001
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发表时间：
2021
期刊：
影响因子：
7.3
通讯作者：
Xianyin Duan
中科院分区：
文献类型：
--
作者：
Xin Lin;Kunpeng Zhu;Jerry Ying Hsi Fuh;Xianyin Duan
与其他添加剂制造工艺相比，基于金属的添加剂制造可以制造出精度更高、密度更高的零件，在汽车、医疗、航空航天等行业的应用中具有独特的优势。然而，铸件的尺寸精度、层形态、机械和冶金缺陷等质量缺陷一直阻碍着MAM技术的广泛应用。这些降低了构建质量的可重复性和一致性。为了克服这些缺点，生产出高质量的零件，在制造过程中进行在线监测和过程控制是非常重要的。为了消除早期缺陷，提高工艺稳定性和最终制造质量，需要一种能自动优化工艺参数的工艺监控系统。本文从文献中选取了当前具有代表性的研究，综述了MAM过程监测与控制的研究进展。本文以MAM监测系统的关键部件为主流，对MAM监测系统、测量与信号采集、信号与图像处理以及用于过程监测和质量分类的机器学习方法进行了研究。讨论和总结了它们的算法实现和应用的优缺点。最后，对MAM过程监控的研究方向进行了展望。
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.