Design of Molten Pool Geometry in Additive Manufacturing by Analysis of Inverse Problems

Abstract Metal additive manufacturi ng is expected to achieve a manufacturing revolution by merging the advance technologies such as information technology and image-sensing technology. The concept on design of process parameters in metal means machine learning discussed present work. In order realize concept, parameter laser tried be predicted from images molten pool geometry analysis inverse problems with learning. It found that prediction pitch width, which one parameters, can achieved probabilistic classification.