Date of Award
2025
Document Type
Open Access Master's Thesis
Degree Name
Master of Science in Materials Science and Engineering (MS)
Administrative Home Department
Department of Materials Science and Engineering
Advisor 1
Paul Sanders
Committee Member 1
Stephen Kampe
Committee Member 2
Dan Seguin
Abstract
Recycling aluminum saves up to 95% of the energy required for primary production but introduces impurities such as iron that limit alloy performance. The Al-Mg-Fe system, though commercially used, remains underexplored. This work applies machine learning with Multi-Objective Bayesian Optimization (MOBO) using Thermo-Calc simulations to design Al-Mg-Fe-Si-Cu-Zn alloys with improved castability and reduced porosity. The optimization targeted freezing range, shrinkage, drag coefficient, and microstructural parameters, identifying compositions with high Mg, Si, and Cu and low Zn as optimal. Predicted alloys were experimentally cast and characterized, with yield strengths exceeding 95 MPa and elongation comparable to commercial 319 aluminum in the as- cast state.
Recommended Citation
Patten, Skylar, "MACHINE LEARNING DESIGN AND EXPERIMENTAL PRODUCTION OF RECYCLED ALUMINUM ALLOYS WITH MAGNESIUM AND IRON", Open Access Master's Thesis, Michigan Technological University, 2025.