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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium Artificial Intelligence Applications in Integrated Computational Materials Engineering
Presentation Title A Machine Learning Informed Phase Field Damage Model to Simulate Void Nucleation and Growth in Metal Microstructures
Author(s) Abhijith Thoopul Anantharanga, Jackson Plummer, Saryu Fensin, Brandon Runnels
On-Site Speaker (Planned) Abhijith Thoopul Anantharanga
Abstract Scope Void nucleation and growth in polycrystalline metals during spall is a complex phenomenon that is not fully understood. This study employs machine learning and phase field modeling to study incipient spall under high-impact dynamic loading conditions. We develop a machine learning model designed to predict potential void nucleation sites in polycrystalline metal microstructures. Validation is performed across various microstructural datasets and synthetic microstructures generated using Generative Adversarial Networks. The machine learning model is used to generate heatmaps that act as void nucleation probability fields, which informs a phase field damage model. The phase field model, based on the minimum dissipation potential, simulates void nucleation and growth due to incipient spall under high-impact dynamic loading conditions, taking into account chemical potential, grain boundary energy, elasticity, and plastic potential.The integration of machine learning predictions with phase field modeling enables accurate simulation of void evolution, demonstrating strong concordance with experimental observations.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Computational Materials Science & Engineering, Other

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Bayesian Approach for Constitutive Model Selection and Calibration Using Diverse Material Responses
A Machine Learning Informed Phase Field Damage Model to Simulate Void Nucleation and Growth in Metal Microstructures
A Multiscale Simulation Framework for Incremental Deformation Processing Using a Recurrent Neural Network Surrogate Model for Crystal Plasticity
A Study on the Smoke Recognition of Steelmaking Plants Based on EL-MobileNet
Accelerating Crystal Plasticity Simulations with Graph Neural Networks
AI in ICME: Methodologies for AI Alignment and Explainability in Self-Driving Labs
ANNA: An Open-Source Platform for Developing Artificial Neural Networks Assistant Potential Enabling High Accurate and Efficient Molecular Dynamics Simulation
Automation of the ICME Workflow Incorporating Material Digital Twins at Different Length Scales Within a Robust Information Management System
Combined THz-TDS and Raman Spectroscopy for In-Situ Material Identification via a Machine Learning Algorithm
Conditional Diffusion Models for Interlocking Metasurface Design
Data-Driven Modeling of Dislocations for Multi-Scale Simulations
Data and Decision Science-Driven Assessment and Selection of Mg Alloys for Fracturing Applications
Data Assimilation of Multi-Phase-Field Model based on Physically Informed Neural Network
Data Modelling of Through-Life Structural Integrity Assessment of Dissimilar Metal Welds for Nuclear Application
Design of High-Strength Steel Using Machine Learning Techniques
Developing a Foundational Inter-Atomic Potential for Transitional Metal Alloys Using Active Learning
Developing Machine Learning Interatomic Potential for Fe-Cr-Ni Alloys
Developing Reduced Order Models for Phase Field Modeling of Irradiation Damage Using Koopman Operator Theory
Digital Twins for Accelerated Materials Innovation
Effect of the Microstructure on Intergranular Fracture in FCC and HCP Polycrystals: A Machine Learning Approach
Enhancing Extrusion Efficiency: Development of a Digital Twin for Glass Reinforced Polymer Processes Using Machine Learning and Real-Time Data Integration
Enhancing Medical Waste Recycling Through Computer Vision and Near-Infrared Spectroscopy
Establishing a Novel Systematic Alloy Design Strategy Based on Large Language Model Framework
Generative Adversarial Network (GAN)-Based Microstructure Mapping from Surface Profile For Laser Powder Bed Fusion (LPBF)
Harnessing Graph Neural Networks for Classification of Unique Glassy Structures in CuZr Metallic Glasses
High-Throughput and Robust Materials Design Hypothesis Generation via a RAG-Enhanced Large Language Model
Machine Learning-Driven Multiscale Analysis of Mechanical Properties in Metal-Matrix Nanocomposites
Machine Learning and High-Throughput Computations Guided Development of High Temperature Oxidation-Resisting Ni-Co-Cr-Al-Fe High-Entropy Alloys
Machine Learning Facilitated Integration of Characterization Data and Simulations to Generate Residual Stress Distributions
Machine Learning Potentials and Other Tools in LAMMPS for Materials Engineering
Magnetic RANN Interatomic Potential for Iron
Physical Metallurgy and Machine Learning Guide the Prediction of Continuous Cooling Phase Transformation in Steels
Prediction of Fatigue Indicator Parameter by Graph Neural Network
Prediction of Material Parameters Using Machine Learning Supported by Large-Scale Phase-Field Simulations of Dendrite Growth
Pushing the Limits of Fine Feature Detection in Deep-Learning Assisted 3D X-Ray Microscopy: Characterization of Hierarchical Microstructures in TiC Reinforced Nickel Matrix Composites
Rapid Microstructural Determination from Nano-indentation of High Entropy Alloys Using Machine Learning and Genetic Algorithms
Starrydata Explorers: Visualization Platforms to Overview the Past Reported Experimental Samples
Sustainable Aluminum Alloy Design via Computer Vision
Towards Automatic Alloy Design via Large Language Model Powered Multi-Agent Collaborations
Tuning Fracture Characteristics for Chiral Aperiodic Monotile Based Composites by Employing Multi-Objective Bayesian Optimization
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