(286c) Design Space Exploration and Optimization for Additive Manufacturing through Rational Feature Engineering and Machine Learning

Additive manufacturing is an emerging technology in which parts are fabricated layer-by-layer from computer-aided design models. Oxide dispersion strengthened (ODS) steels, the material subject of this work, are known for their high strength and resistance to radiation damage, making them ideal for structural materials in aerospace applications and in advanced fission and fusion reactors [1-2]. Pulse wave laser emission, as compared to continuous wave emission, can be used to improve surface quality and refine microstructure of the manufactured ODS steel parts, but it also leads to a much-increased design parameter space [3-5]. In this work, we perform rational feature engineering to find features that allow us to accurately and robustly classify “good” and “bad” quality parts. We then extend this exploration into a regression problem, using optical density and nano-indentation values. Finally, we determine the parameter ranges that consistently produce “good” quality, high-strength parts and describe the physical phenomena that explain these findings.

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