A Deep Learning Approach to Predict Full-Field Stress Distribution in Composite Materials

This thesis proposes a deep learning approach to predict stress at various stages of mechanical loading in 2-D representations of fiber-reinforced composites. More specifically, the full-field stress distribution at elastic and at an early stage of damage initiation is predicted based on the microstructural geometry. The required data set for the purposes of training and validation are generated via high-fidelity simulations of several randomly generated microstructural representations with complex geometries. Two deep learning approaches are employed and their performances are compared: fully convolutional generator and Pix2Pix translation. It is shown that both the utilized approaches can well predict the stress distributions at the designated loading stages with high accuracy.

Name thesis:degree_name

M.S.

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science

Department dc:contributor.department

Computer Science and Application

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2021

Author dc:creator

• Sepasdar, Reza

Chairs dc:contributor.committeechair

• Karpatne, Anuj
• Shakiba, Maryam

Committee member dc:contributor.committeemember

• Huang, Lifu

• Deep learning (Machine learning)
• CNN
• Full-Field Prediction
• Image-to-Image Translation
• Adversarial Learning

Statement dc:rights

• Creative Commons Attribution 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/10919/103427

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/103427