Optical process monitoring for Laser-Powder Bed Fusion (L-PBF)

The Laser Powder Bed Fusion (L-PBF) process is being adopted in different industrial fields. However, L-PBF currently lacks process reproducibility and quality. Hence, quality monitoring techniques need to be adopted in order to reduce the process variability and to ensure a high-quality process. Accordingly, this work proposes a quality monitoring approach based on machine learning which links the optical signal of a layer to the density of the final part. The approach consists of selecting relevant statistical features from optical data and validating these features by assessing their ability in predicting the different density classes of different products. Afterwards, the approach is compared to a new deep learning framework that allows predicting a part density from the corresponding raw optical signals. This comparison allows assessing the relevance of the identified statistical features. The proposed monitoring approach is applied on cubical specimens produced with different process parameters, and the results are then discussed and analyzed.