Date of Award
8-2025
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Mechanical and Civil Engineering
First Advisor
Soheil Saedi
Second Advisor
Kegang Wang
Third Advisor
Sayed Ehsan Saghaian
Fourth Advisor
Mirmilad Mirsayar
Abstract
This dissertation provides a data-driven system integrating synthetic data generation and machine learning (ML) techniques to create multicomponent SMA compositions with specific transformation temperatures (TTs). Models were trained to represent the nonlinear dependencies influencing martensitic transformation behavior by using elemental, thermodynamic, and process-related aspects. The capacity of the ML models on medium entropy NiTiHfPd and high entropy NiTiHfZrCu systems accuracy was confirmed by experimental validation showing TTs closely matched with model outputs.
Recommended Citation
Raji, Hatim, "Shape Memory Behavior in Medium to High Entropy Shape Memory Alloys: Design, Prediction, and Experimental Analysis" (2025). Theses and Dissertations. 1578.
https://repository.fit.edu/etd/1578
Included in
Computer-Aided Engineering and Design Commons, Mechanics of Materials Commons, Metallurgy Commons, Other Materials Science and Engineering Commons, Structural Materials Commons