Predicting laser powder bed fusion defects through in-process monitoring data and machine learning

Abstract

Industry application of additive manufacturing demands strict in-process quality control procedures and high product quality. Feedback loop control is a reasonable solution and a necessary tool. This paper demonstrated our preliminary work on the laser powder-bed fusion feedback loop: predict local porosity through in-process monitoring images and machine learning. 3D models were rebuilt from in-situ optical tomography monitoring images and post-build X-ray CT images. They were registered to the original CAD. Dataset for machine learning was assembled from those registered 3D models. The trained machine learning model can precisely predict local porosity caused by lack of fusion and keyhole with multi-layer monitoring images. It also indicates the optimal processing window. It is impossible to be sure about the occurrence of defects in a layer based only on the abnormality of a single layer, and vice versa. Defects in a layer can be caused by improper parameters or anomalies in current layer or subsequent layers; defects in one layer can also be eliminated by proper parameters in the following layers. The work laid the basis for the next step feedback loop control of pore defect.