Accelerating Additive Design With Probabilistic Machine Learning

Abstract

Additive manufacturing (AM) has been growing rapidly to transform industrial applications. However, the fundamental mechanism of AM has not been fully understood which resulted in low success rate of building. A remedy is to introduce surrogate modeling based on experimental dataset to assist additive design and increase design efficiency. As one of the first papers for predictive modeling of AM especially direct energy deposition (DED), this paper discusses a bidirectional modeling framework and its application to multiple DED benchmark designs including: (1) forward prediction with cross-validation, (2) global sensitivity analyses, (3) backward prediction and optimization, and (4) intelligent data addition. Approximately 1150 mechanical tensile test samples were extracted and tested with input variables from machine parameters, postprocess, and output variables from mechanical, microstructure, and physical properties.