Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Hansen, A. Eric
Committee Member
Horstemeyer, Mark
Committee Member
Boggess, Julian
Committee Member
Chu, Yul
Date of Degree
8-11-2007
Document Type
Graduate Thesis - Open Access
Major
Computer Engineering
Degree Name
Master of Science
College
James Worth Bagley College of Engineering
Department
Department of Electrical and Computer Engineering
Abstract
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.
URI
https://hdl.handle.net/11668/15343
Recommended Citation
AbuOmar, Osama Yousef, "Artificial neural network modeling of flow stress response as a function of dislocation microstructures" (2007). Theses and Dissertations. 730.
https://scholarsjunction.msstate.edu/td/730