Semester
Spring
Date of Graduation
2019
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Mechanical and Aerospace Engineering
Committee Chair
Stefanos Papanikolaou
Committee Co-Chair
Ever Barbero
Committee Member
Ever Barbero
Committee Member
Nasser Nasrabadi
Abstract
The present thesis makes a connection between spatially resolved strain correlations and material processing history. Such correlations can be used to infer and classify prior deformation history of a sample at various strain levels with the use of Machine Learning approaches. A simple and concrete example of uniaxially compressed crystalline thin films of various sizes, generated by two-dimensional discrete dislocation plasticity simulations is examined. At the nanoscale, thin films exhibit yield-strength size effects with noisy mechanical responses which create an interesting challenge for the application of Machine Learning techniques. Moreover, this thesis demonstrates the prediction of the average mechanical responses of thin films based on the classified prior deformation history and discusses the possible ramifications for modelling crystal plasticity behavior in extreme settings.
Recommended Citation
Tzimas, Michail, "Deformation Correlations and Machine Learning: Microstructural inference and crystal plasticity predictions" (2019). Graduate Theses, Dissertations, and Problem Reports. 3827.
https://researchrepository.wvu.edu/etd/3827