Deep Learning for Melt Pool Depth Contour Prediction From Surface Thermal Images via Vision Transformers

arXiv:2404.17699v1 Announce Type: new
Abstract: Insufficient overlap between the melt pools produced during Laser Powder Bed Fusion (L-PBF) can lead to lack-of-fusion defects and deteriorated mechanical and fatigue performance. In-situ monitoring of the melt pool subsurface morphology requires specialized equipment that may not be readily accessible or scalable. Therefore, we introduce a machine learning framework to correlate in-situ two-color thermal images observed via high-speed color imaging to the two-dimensional profile of the melt pool cross-section. Specifically, we employ a hybrid …

abstract arxiv contour cs.cv cs.lg deep learning defects equipment fusion images melt monitoring performance pool prediction surface transformers type via vision vision transformers