Laser powder bed fusion (LPBF) has shown promise for wide range of applications due to its ability to fabricate freeform geometries and generate a controlled microstructure. However, components generated by LPBF still possess sub-optimal mechanical properties due to the defects that are created during laser-material interactions. In this work, we investigate mechanism of spatter formation, using a high-fidelity modelling tool that was built to simulate the multi-physics phenomena in LPBF. The modelling tool have the capability to capture the 3D resolution of the meltpool and the spatter behavior. To understand spatter behavior and formation, we reveal its properties at ejection and evaluate its variation from the meltpool, the source where it is formed. The dataset of the spatter and the meltpool collected consist of 50 % spatter and 50 % melt pool samples, with features that include position components, velocity components, velocity magnitude, temperature, density and pressure. The relationship between the spatter and the meltpool were evaluated via correlation analysis and machine learning (ML) algorithms for classification tasks. Upon screening different ML algorithms on the dataset, a high accuracy was observed for all the ML models, with ExtraTrees having the highest at 96 % and KNN having the lowest at 94 %.

翻译：激光粉末床熔融（LPBF）因其制造自由形态几何结构及生成可控微观组织的能力，在众多应用中展现出广阔前景。然而，由于激光-材料相互作用过程中产生的缺陷，LPBF制备的构件仍存在次优的力学性能。本研究利用高保真建模工具模拟LPBF中的多物理现象，探究飞溅形成机理。该建模工具能够捕捉熔池及飞溅行为的三维分辨率特征。为理解飞溅行为与形成机制，我们揭示了飞溅喷射时的特性，并评估其与形成源——熔池之间的差异。采集的飞溅与熔池数据集包含50%飞溅样本和50%熔池样本，特征参数涵盖位置分量、速度分量、速度幅值、温度、密度及压力。通过相关性分析及机器学习（ML）分类算法，评估了飞溅与熔池之间的关联性。在数据集上对比不同ML算法后，所有模型均展现出高精度，其中ExtraTrees算法精度最高达96%，KNN算法精度最低为94%。