Mohadeseh Taheri-Mousavi

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Title: Hybrid physics-constrained machine learning-enhanced design of an additively manufacturable Al alloy with high-temperature strength

Speaker: Mohadeseh Taheri-Mousavi, assistant professor, materials science and engineering

Aluminum alloys that exhibit high strength and creep resistance at high temperatures can be our next-generation fan blades of jet engines and pistons of combustion engines. However, there is macro-segregation of heavy elements in Al during casting. Additive manufacturing of these alloys is also traditionally challenging due to the presence of hot cracking. We demonstrate a physics-constrained, hybrid machine learning framework with data generated from the calculation of phase diagram (CALPHAD)-based integrated computational materials engineering (ICME) techniques to explore the compositional space of Al-Zr-Er-Y-Yb-Ni and identify an optimal alloy composition, achieving maximum predicted strength at a temperature of 250ºC. Using only 40 sampling data with our most efficient machine learning algorithm (neural network), we predict a microstructure with 3.5 X higher stability of nanoscale hardening phases than a record printable Al-alloy. We fine-tuned the composition by developing a rapid experimental workflow of hardness tests and laser-scanned induction melting to analyze potential cracking. Our 3D-printed samples from the optimal composition show high thermal stability and 50% higher strength than patented printable Al, reaching the strength of 7000 Al series. The combined numerical and experimental techniques provide an efficient and robust pathway for transformative future alloy hybrid machine learning/CALPHAD design by various manufacturing techniques, especially additive manufacturing.

Biography

S. Mohadeseh Taheri-Mousavi joined CMU in September 2022 as an assistant professor from MIT where she was a postdoctoral associate jointly in the Departments of Mechanical Engineering and Materials Science and Engineering. Before that, she was a postdoctoral fellow at Brown University. She received her Ph.D. from EPFL, Switzerland, and her B.Sc. and M.Sc. from Sharif University of Technology, Iran. Her research interests lie in developing novel multi-scale numerical and analytical frameworks in combination with machine learning techniques to discover next-generation structural alloys produced by various manufacturing techniques (especially additive manufacturing) and under extreme environmental conditions. She received both early and advanced prestigious Swiss National Science Foundation fellowships for her postdoctoral studies at Brown and MIT.