Characteristics of droplet spatter behavior and process-correlated mapping model in laser powder bed fusion

Abstract

The spatter behavior in the laser powder bed fusion (L-PBF) additive manufacturing is unavoidable owing to the interaction between high-energy laser beam and powder particles. And the droplet spatters generated by the laser induction not only directly reflect the steady-state of the micro-molten pool but also directly affect the processing quality of the L-PBF process. In order to further understand the mechanism of laser–powder interaction, and the generation and evolution of droplet spatter, the paper presents a droplet spatter feature information extraction algorithm and a manufacturing status classification method. Three kinds of feature information of droplet spatter were collected, extracted, and discussed by high-speed imaging, image processing, and statistical analysis techniques. The evolution mechanisms of the molten pool, the keyhole, and the droplet spatter behavior under different linear energy density inputs were discussed in combination with the single-track build. A mapping model of the droplet spatter behavior characteristics and manufacturing status was processed after that. The accuracy and reliability of the mapping model were verified and analyzed by using the AdaBoost CART classification model. The classification model not only verified the mapping mechanism, and the findings provide a basis and reference for the quality control of the L-PBF process in the future.