Fast predictive model of crystallographic texture evolution in metal additive manufacturing

Abstract

This communication introduces a fast material- and process-agnostic modeling approach, not reported in the open literature, that is calibrated for predicting the evolution of texture in metal additive manufacturing of stainless steel 304L as a function of a process parameter, namely the laser scanning speed. The outputs of the model are compared against independent validation experiments for the same material system and show excellent consistency. The model also predicts a trend in the change of texture intensity as a function of the process parameter. The major novelty and strength of this work is the model’s speed and extremely light computational load. The model’s calibrations and predictions were carried out in 9.2 s on a typical desktop computer.