Structure-Property Prediction for Magnetorheological Elastomer Using Machine Learning Approaches

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" Structure-Property Prediction for Magnetorheological Elastomer Using Machine Learning Approaches "
Feng, Shengwei Sun, Lizhi

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	1105127
Doc. No	:	TLpq2299003558
Main Entry	:	Feng, Shengwei
	:	Sun, Lizhi
Title & Author	:	Structure-Property Prediction for Magnetorheological Elastomer Using Machine Learning Approaches\ Feng, ShengweiSun, Lizhi
College	:	University of California, Irvine
Date	:	2019
student score	:	2019
Degree	:	M.S.
Page No	:	99
Abstract	:	Magnetorheological elastomer (MRE) is a rubbery composite material filled with micron-sized ferromagnetic particles whose mechanical properties can be tailored by the application of external magnetic fields. Due to its magnetic and mechanical coupling effect, MRE is increasingly used in the field of engineering. Capturing the responses of MRE is essential for materials modeling and can be reached either by the physics-based finite element modeling or data-based artificial intelligence modeling. In this thesis, machine learning-based data-driven models are built to discover the structure-property linkages of MRE. The proposed method employs a pre-trained Convolutional Neural Network (CNN) and also an artificial neural network (ANN) to evaluate the critical features of the material microstructures that lead to precise predictions for the critical mechanical properties of MRE. It has been proven that these approaches can make compelling predictions while dramatically reduce the time needed for the calculation process. With low computation cost, the machine learning models also exhibit great potential in microstructure optimization.
Subject	:	Civil engineering
	:	Engineering