James Hanagan is a Ph.D. student working on computational alloy design geared toward functionally graded materials (FGMs). Originally from State College, PA, he graduated from Penn State University in 2022 with a Bachelor’s degree in Materials Science and Engineering. His past research experience includes experimental work on metal additive manufacturing (AM), with his undergraduate honors thesis being on the exploration of processing parameters for laser powder bed fusion (LPBF) of TZM, a molybdenum alloy.
Elyse Harman
Elyse Harman is a Materials Science & Engineering major graduating in 2021, and a participant in Dr. Arróyave’s group since 2019. Elyse models phase changes during alloy solidification by the phase-field method. These models produce predictions for the microstructure evolution during additive manufacturing, which informs the tailoring of final properties. Her other research interests include numerical methods, plasma physics, and optics.
Oussama Hattab
Pejman Honarmandi
Post-doctoral researcher
Arroyave Research Group, Texas A&M U.
Office: Doherty Bldg., A301
Phone:
E-Mail: hona107@tamu.edu
I am from Iran and currently a post-doctoral researcher of Materials Science & Engineering at Texas A&M University. I received a bachelor’s degree from Sharif University of Technology (SUT), and subsequently a master’s degree from Khaje Nasir Toosi University of Technology (KNTU). I am proud to declare that I was recognized as a top graduate student among the graduates of academic year of 2008-2009, and honored for obtaining the highest total GPA.
Because of my intense enthusiasm for investigating in different fields of materials science, I did various experimental and theoretical research during my past education, including “assessment of surface resistance in polymeric nano-composite materials”, “evaluation of hot deformation behavior in steels and titanium alloys, “synthesis, characterization, and microscopic analysis of different types of nano-materials”, etc, which led to the publication of different ISI and conference papers.
I joined Prof. Arroyave’s group in Fall 2014, and my research field of interest is alloy design under uncertainty, including modeling and Bayesian uncertainty analysis of plastic flow behavior of low-alloy TRIP-assisted steels, and precipitation kinetics of NiTi based shape memory alloys.
Sina Hossein Zadeh
Sina joined Dr. Arroyave’s Group in Fall 2021. He is interested in understanding materials behavior through computational material science.
Webpage: https://www.sina.science
Xueqin Huang
Md Shafiqul Islam
Jaylen James
Graduate Research Assistant
Arroyave Research Group, Texas A&M U.
Office: Doherty Bldg., A301
Phone:
E-Mail: jaylen_james@tamu.edu
Jaylen James graduated from Priarie View A&M University with a Bachelors degree in Mechanical Engineering. He is now pursuing his PhD. Currently, his research focus is in fusing information from multi-fidelity sources using model reification. This project is in collaboration with the Air Force Research Laboratory. The methods Jaylen is working on will help researchers better predict material properties and help enhance the material design process for various applications.
Danial khatamsaz
Postdoctoral researcher
Arroyave Research Group, Texas A&M U.
Office: EIC bldg., room 113D
Phone: n/a
E-Mail: danialkh26@tamu.edu
Tanner Kirk
Graduate Research Assistant
Design Systems Laboratory, Texas A&M
Office: Doherty Bldg., 309
Email: tannerkirk@tamu.edu
Tanner Kirk is a Ph.D. student in the Mechanical Engineering Department at Texas A&M University. He is co-advised by Dr. Richard Malak and Dr. Raymundo Arroyave. Tanner researches the use of robotic path planning algorithms to design Functionally Graded Materials (FGMs). The methods Tanner develops use thermodynamic predictions of phase formation (CALPHAD) to plan FGMs that avoid the deleterious phases that often plague the manufacturing of FGMs. These methods are scalable to FGMs composed of an arbitrary number of elements and can be used to optimize FGMs for performance. Tanner’s other research interests are machine learning, optimization, and materials design.