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.

Abstract of the Dissertation

Multiscale Magneto-mechanical Coupling Framework of Magnetorheological Elastomer Composites By Shengwei Feng Doctor of Philosophy in Civil Engineering University of California, Irvine, 2024 Professor Lizhi Sun, Chair

This dissertation introduces a multiscale modeling and simulation framework for studying magnetorheological elastomer (MRE) composites, effectively bridging the gap between detailed microscopic modeling and experimental findings. Preliminary investigations focus on understanding the baseline magneto-mechanical properties of MREs, setting the stage for deeper inquiries into specific behaviors. Subsequent simulations integrate viscoelastic and hyperelastic properties to examine how the models respond under cyclic loading, with a particular focus on behavior that depends on magnetic field strength and strain variations. By scrutinizing microstructural influences such as particle distribution and interface conditions, the research elucidates how these factors affect MRE responses to magnetic fields and mechanical stresses. Modeling and simulations reveal that interface friction and particle dynamics are crucial in determining MREs' damping characteristics and overall stability. Notably, the study identifies how variations in interfacial interactions under different magneto-mechanical conditions significantly impact the performance and reliability of MRE composites. This comprehensive analysis deepens our understanding of MRE behavior and paves the way for optimizing the design and application of these smart materials in adaptive systems, potentially transforming their use in various industrial and technological sectors.