Dr. Matthew Kasemer – College of Engineering

Assistant Professor

Mechanical Engineering

1008 North Engineering Research Center
(205) 348-0044
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Education

Ph.D., Mechanical Engineering, Cornell University, 2018
M.S., Mechanical Engineering, Cornell University, 2015
B.S., Mechanical Engineering, Rochester Institute of Technology, 2012

Biography

Dr. Kasemer’s research interests lie primarily in developing, implementing, and utilizing computational models to predict the deformation response of metals subjected to loading, with a specific focus on polycrystalline structural alloys. Results from computational modeling lend insight into the evolution of the elastic and plastic deformation responses at both the crystal scale and the sample scale, and the fundamental micromechanical mechanisms governing this behavior. This allows for a better understanding of the relationship between microstructure and the attendant mechanical properties of the material.

Honors and Awards

NSF CAREER Award, National Science Foundation, 2022

Areas of Research

Latest Publications

D.C. Pagan, C.R. Pash, A.R. Benson, M. Kasemer, Graph neural network modeling of grain-scale anisotropic elastic behavior using simulated and measured microscale data, npj Computational Materials 8 (2022) 259.
J. He, D. Borisov, J. Fleming, M. Kasemer, Subsurface polycrystalline reconstruction based on full waveform inversion – A 2D numerical study, Materialia 24 (2022) 101482.
J. Fuhg, L. van Wees, M. Obstalecki, P. Shade, N. Bouklas, M. Kasemer, Machine-learning convex and texture-dependent macroscopic yield from crystal plasticity simulations, Materialia 23 (2022) 101446.
J. Cappola, J.C. Stinville, M.A. Charpagne, P.G. Callahan, M.P. Echlin, T.M. Pollock, A. Pilchak, M. Kasemer, “On the Localization of Plastic Strain in Microtextured Regions of Ti-6Al-4V”, Acta Materialia, 204 (2021), 116492.
M. Kasemer, P. Dawson, ”A finite element methodology to incorporate kinematic activation of discrete deformation twins in a crystal plasticity framework,” Computer Methods in Applied Mechanics and Engineering 358 (2020), 112653.