ProgramMaster Logo
Conference Tools for MS&T24: Materials Science & Technology
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting MS&T24: Materials Science & Technology
Symposium Integrated Computational Materials Engineering for Physics-Based Machine Learning Models
Presentation Title Phase-Field Modeling of Grain Evolution and Recrystallization in Friction Stir Processing
Author(s) Zhengtao Gan
On-Site Speaker (Planned) Zhengtao Gan
Abstract Scope We developed a three-dimensional (3D) phase-field model to simulate the mesoscopic grain evolution during extreme grain deformation and recrystallization in friction stir processing (FSP). A critical feature of FSP is that the velocity field introduced by the rotating tool deforms the crystalline grains in the material. Our model captures this dynamic process through introducing the advection equation. Another feature of FSP is the nucleation and dynamic recrystallization (DRX) due to large grain deformation. These phenomena are simulated in our model, where the evolution of dislocation density is modeled by the Laasraoui-Jonas (LJ) model, nucleation is determined by the magnitude of the dislocation energy and DRX grain growth is involved in the phase-field framework. The solver is implemented on the Google JAX platform for GPU computing for enhancing the computational capacity. The model has been validated against experimental data based on the ratio of initial to final grain sizes.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Multiscale Simulation Investigation of Cavity Evolution in a Ni TPBAR Coating
Advanced Coupling of an FFT-Based Mesoscale Modeling Method to a Macroscale Finite Element Method
B-1: Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform
Deep Generative Model for Reproducing Microstructure of Low-Carbon Steel During Continuous Cooling
Deep Learning for Early Detection and Localization of Damage in Metal Plates
Developing Data-Driven Strength Models Incorporating Temperature and Strain-Rate Dependence
Hybrid Machine Learning Informed Design Guidelines for Structural Gradient Alloys with Enhanced Performances
Phase-Field Modeling of Grain Evolution and Recrystallization in Friction Stir Processing
PRISMS-MultiPhysics: An Open-Source Coupled Phase Field-Crystal Plasticity Framework and its Application to Simulate Twinning in Magnesium Alloys
Thermodynamic Integration for Dynamically Unstable Systems Using Interatomic Force Constants without Molecular Dynamics
Utilizing Convex Neural Networks to Predict the Yield Surfaces of Polycrystalline Samples with Complex Crystallographic Textures

Questions about ProgramMaster? Contact programming@programmaster.org