Machine Learning Algorithms on Density Prediction of Electron Beam Selective Melted Parts

Electron Beam Selective Melting(EBSM) is a novel additive manufacturing technology, which is developing very fast nowadays. It has many advantages: building up parts with complex morphology; processing under vacuum to get rid of the impurity; its manufactured part with small residual stress and powder recycling. However, it is difficult to establish the relationship between EBSM's processing parameters and parts' properties. Here a series of Inconel 718 cubic specimens are manufactured through adjusting EBSM's scanning speed, beam current, plate temperature and layer thickness. Then the densities of these specimens are measured. Three kinds of machine learning algorithms, including linear regression, support vector regression and neural network, have been utilized to build the relationship between these four processing parameters and density. The results show that: Linear regression has the worst prediction skill as a result of its small model capacity; neural network has a better prediction accuracy, but it is easily overfitting; support vector regression has appropriate model capacity and good physical interpretation, and it behaves best in the density prediction.