Argonne scientists use machine learning to predict defects in 3D printed parts

A team of researchers from Argonne National Laboratory and Texas A&M University have developed an innovative new approach to defect detection in 3D printed parts. Using real-time temperature data, together with machine learning algorithms, the scientists were able to make correlative links between thermal history and the formation of subsurface defects during the laser powder … Continue reading Argonne scientists use machine learning to predict defects in 3D printed parts