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

Abstract

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.

Keywords: Condition monitoring; Machine learning; Measurement and control; Metal-based additive manufacturing.