Optimizing laser powder bed fusion of Ti-5Al-5V-5Mo-3Cr by artificial intelligence

Abstract

The prerequisite for exploiting the full potential of additive manufacturing (AM) is the rapid and cost-effective fabrication of defect-free components. However, each newly processed material usually requires the identification of the optimal parameter set, a cost and time-consuming process, mostly conducted by trial and error. Here, an optimization strategy based on artificial intelligence (AI) is developed for predicting the density of additively manufactured Ti-5Al-5V-5Mo-3Cr components from experimental data. The present approach opens the way to a faster identification of the optimum set of processing parameters via AI. (C) 2020 Elsevier B.V. All rights reserved.