Theses and Dissertations

Issuing Body

Mississippi State University


Hansen, A. Eric

Committee Member

Horstemeyer, Mark

Committee Member

Boggess, Julian

Committee Member

Chu, Yul

Date of Degree


Document Type

Graduate Thesis - Open Access


Computer Engineering

Degree Name

Master of Science


James Worth Bagley College of Engineering


Department of Electrical and Computer Engineering


An artificial neural network (ANN) is used to model nonlinear, large deformation plastic behavior of a material. This ANN model establishes a relationship between flow stress and dislocation structure content. The density of geometrically necessary dislocations (GNDs) was calculated based on analysis of local lattice curvature evolution. The model includes essential statistical measures extracted from the distributions of dislocation microstructures, including substructure cell size, wall thickness, and GND density as the input variables to the ANN model. The model was able to successfully predict the flow stress of aluminum alloy 6022 as a function of its dislocation structure content. Furthermore, a sensitivity analysis was performed to identify the significance of individual dislocation parameters on the flow stress. The results show that an ANN model can be used to calibrate and predict inelastic material properties that are often cumbersome to model with rigorous dislocation-based plasticity models.