Transfer learning-based quality monitoring of laser powder bed fusion across materials

Abstract

Laser powder bed fusion (LPBF) is a promising additive manufacturing (AM) technique that has gained rapid growth. Despite the unique advantages such as producing complex-shaped metal parts, LPBF has been still slowed by the poor process repeatability and part consistency. In-situ monitoring methods combining sensing sensors and machine leaning (ML) tools have attracted much attention for defect detection and quality identification. However, the current ML algorithms usually rely on the availability of massive labeled datasets, and the trained model for monitoring a certain powder material cannot be directly applied to monitoring another material with different properties. Moreover, for a new material, it is time-consuming and expensive to collect the sufficient labeled data for building the quality monitoring model from scratch. To this end, motivated by transfer learning (TL), we propose a TL-based quality monitoring approach across two different materials in the LPBF process. Three different sensing datasets of layer-wise images of the solidified layer, photodiode signals, and acoustic emission signals are separately transferred from the 316L stainless steel to the TC4 titanium alloy. A convolutional neural network (CNN)-based quality monitoring model is first built by the sufficient source datasets from 316L stainless steel material. Then, the pre-trained CNN model is fine-tuned with a small amount of the target datasets for quality monitoring on TC4 titanium alloy material. Experimental results demonstrate the effectiveness of the proposed method for quality monitoring from one material to another with limited target datasets, with a classification accuracy above 92 % just with 5 % of target datasets on all three transferred datasets.