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MNE MS Thesis Defense by Mr. Utiwe Ezekiel

When: Friday, June 2, 2023
3:00 PM - 5:00 PM
Where: > See description for location
Description: Mechanical Engineering MS Thesis Defense by
Mr. Utiwe Ezekiel

June 2, 2023

3:00 p.m. - 5:00 p.m.

Virtual, Zoom link:

Meeting ID:
967 9707 8382

Passcode: 508999

Deep Learning to Predict Full-field Nonlinear Plastic Response of Nanocomposites

Predicting full-field mechanical responses accurately and efficiently is of fundamental importance to assess materials failure and has various applications in design optimization, uncertainty quantification, and structural health monitoring. The classical FE^2 or global-local scheme can be costly, especially for nonlinear plasticity and damage problems. On the other hand, the homogenization method is efficient for overall mean-field results but fails to capture local full-field responses, which can be critical for materials failure. In this study, deep learning methods were developed to predict full-field plastic responses and in particular the complicated nonlinear localized plastic shear band patterns in nanocomposites. A Montel Carlo algorithm is used to automatically generate random geometries representing material microstructures of Al/SiC nanocomposites. The models were subjected to pure shear loading of macroscopically uniform boundary conditions admitted by the Hill-Mandel condition in micromechanics.

Nonlinear elastoplastic simulations were then performed in commercial finite element software ABAQUS to generate inhomogeneous full-field stress/strain responses for data collection and validation. A systematic workflow was created to automate the model generation, finite element simulations, postprocessing, and data curation of response field images for machine learning. After that, a deep learning model of conditional Generative Adversarial Neural Network (cGAN) was developed to predict the full-field plastic response and especially capture the localized plastic shear band patterns. A robust training data set augmented with cases under various rotation transformations has been implemented to consolidate an unbiased training and ensure the symmetry/objectivity of deep learning models. The proposed image-based deep learning methods can be valuable to researchers deploying data-driven models in many other engineering applications involving large scale nonlinear full-field predictions.

Dr. Jun Li, Assistant Professor of Mechanical Engineering,
College of Engineering, UMassD

-Dr. Wenzhen Huang, Professor of Mechanical Engineering, College of Engineering, UMassD
-Dr. Alfa Heryudono, Associate Professor of Department of Mathematics, UMassD

Open to the public. All MNE students are encouraged to attend.

For more information, please contact Dr. Jun Li (
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