Searching for Unknown Material Properties for AM Simulations - Aaron Flood; Rachel Boillat; Sriram Praneeth Isanaka; Frank Liou (Metals, 2023)
Additive manufacturing (AM) simulations are effective for materials that are well characterized and published; however, for newer or proprietary materials, they cannot provide accurate results due to the lack of knowledge of the material properties. This work demonstrates the process of the application of mathematical search algorithms to develop an optimized material dataset which results in accurate simulations for the laser directed energy deposition (DED) process. This was performed by first using a well-characterized material, Ti-64, to show the error in the predicted melt pool was accurate, and the error was found to be less than two resolution steps. Then, for 7000-series aluminum using a generic material property dataset from sister alloys, the error was found to be over 600%. The Nelder–Mead search algorithm was then applied to the problem and was able to develop an optimized dataset that had a combined width and depth error of just 9.1%, demonstrating that it is possible to develop an optimized material property dataset that facilitates more accurate simulation of an under-characterized material.