Process optimization and mechanical property investigation of Inconel 718 manufactured by selective electron beam melting

Abstract

To accelerate the optimization of selective electron-beam melting (SEBM) processing parameters, two machine learning models, Gaussian process regression, and support vector regression were applied in this work to predict the relative density of Inconel 718 from experimental data. The experimental validation indicated that the trained algorithms can precisely predict the relative density of SEBM samples. Moreover, the effects of different parameters on surface integrity, internal defects, and mechanical properties are discussed in this paper. The Inconel 718 samples with high density (>99.5%) prepared by the same SEBM energy density exhibit different mechanical properties, which are related to the existence of the unmelted powder, Laves phase, and grain structure. Finally, Inconel 718 sample with superior strength and plasticity was fabricated using the optimized processing parameters.