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 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.

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