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3:00 PM
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5:00 PM
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MNE MS Thesis Defense by Mr. Utiwe Ezekiel
- Location: > See description for location
- Contact: Mechanical Engineering Department
- Description: Mechanical Engineering MS Thesis Defense by
Mr. Utiwe Ezekiel
DATE:
June 2, 2023
TIME:
3:00 p.m. - 5:00 p.m.
LOCATION:
Virtual, Zoom link: https://umassd.zoom.us/j/96797078382?pwd=VWxjZW1HU2k3cVZvUW94aFFXM0RhUT09
Meeting ID:
967 9707 8382
Passcode: 508999
TOPIC:
Deep Learning to Predict Full-field Nonlinear Plastic Response of Nanocomposites
ABSTRACT:
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.
ADVISOR:
Dr. Jun Li, Assistant Professor of Mechanical Engineering,
College of Engineering, UMassD
COMMITTEE MEMBERS:
-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 (jun.li@umassd.edu).
- Topical Areas: Faculty, General Public, Staff and Administrators, Students, Students, Graduate, Students, Undergraduate, University Community, College of Engineering, Mechanical Engineering, Lectures and Seminars
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2:00 PM
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3:00 PM
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Summer Financial Aid FAFSA Help Zoom Labs
- Location: > See description for location
- Contact: > See Description for contact information
- Description: Financial Aid Services wants to remind all students to file their FAFSA! Join Financial Aid Services for FAFSA Help Labs via Zoom on Fridays from 2-3pm for help filing your FAFSA and learning more about financial aid.
Contact Mark Yanni
myanni@umassd.edu
Join Zoom Meeting
https://umassd.zoom.us/j/97888455259?pwd=MjNiSmsvY2N0Mk1UNGhSL2ttM0g2UT09
- Topical Areas: Students, Students, Graduate, Students, Law, Students, Undergraduate, Financial Aid
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Online Teaching and Learning Strategies
- Location: Online
- Contact: CITS Instructional Development
- Description: A rigorous four-week, fully online certification course that introduces faculty to the current research and best practices for online teaching and learning. Using their own discipline-specific course materials for activities, faculty will work independently, collaboratively with peers from across campus, and with Instructional Designers to design and build one unit of online instruction in a myCourses site. This unit will meet the Quality Online Course Review Rubric criteria and be a model that faculty can reference and replicate as they continue to develop their upcoming fully online course(s).
- Topical Areas: Training, Workshop, audience: Faculty
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