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Meeting MS&T22: Materials Science & Technology
Symposium Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
Presentation Title AI/ML-Driven Multi-Scale Modeling and Design of Structural Materials
Author(s) Pinar Acar, Sheng Liu, Mahmudul Hasan, Arulmurugan Senthilnathan, Hengduo Zhao
On-Site Speaker (Planned) Pinar Acar
Abstract Scope This study addresses the role of artificial intelligence/machine learning (AI/ML) in the multi-scale modeling and design of structural materials. Of particular interest is the design of microstructures to improve the mechanical properties at the component level with the help of data-driven and physics-based/informed AI/ML approaches. Example applications will be discussed for the design of cellular mechanical metamaterials (CMMs) and polycrystalline microstructures (PMs). The CMM applications will explore the extreme properties of the representative volume elements (RVEs) and perform inverse design to achieve target RVE properties using Generative Adversarial Networks (GANs). The PM applications will study the large deformation (crystal plasticity) behavior by imposing constraints arising from the microstructural orientation space using physics-based/informed ML and explore the processing-(micro)structure-property relationships of conventionally and additively manufactured aerospace-grade alloys using Gaussian Process Regression.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Addressing Data Needs for High Temperature Material Processing with Natural Language Processing
AI Driven Microscopic Analysis to Predict the Local Structure in Zirconia Ceramics
AI/ML-Driven Multi-Scale Modeling and Design of Structural Materials
B-5: Using Computer Vision and Machine Learning to Characterize Melt Pool Geometry in Additive Manufacturing
Comparison of Data Driven and Physics-informed Machine Learning Models for Temperature Prediction of Shear Assisted Processing and Extrusion
Composition and Property Prediction of Polymer-derived Silicon Oxycarbides
Computational and Machine Learning Studies of DNA-templated Dye Aggregate Design
Data-Driven Study of Shape Memory Behavior of Multi-component Ni-Ti Alloys
Graph Neural Network Modeling of Deforming Polycrystals
High-throughput Machine Learning Experiments with Graph Neural Networks for Predicting Abnormal Grain Growth in Polycrystalline Materials
Large Scale Atomistic Simulation of the B1-B4 Phase Transition of GaN with the Machine Learning Potential
Machine Learning Based Prediction of Cation Distribution in Complex Spinel Oxides as a Function of Processing Temperature
Machine Learning for Joint Quality Performance-determining Relationship between Intermetallic Properties and weld Microstructure of Al/steel Resistance Spot Welds
Microstructure Characterization and Reconstruction by Deep Learning Methodology
Unraveling the Process Fundamentals of Additive Friction Stir Deposition by Integrating Physics Simulation with Data-driven Approaches
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