An asynchronous parallel high-throughput model calibration framework for crystal plasticity finite element constitutive models

03/14/2023
by   Anh Tran, et al.
0

Crystal plasticity finite element model (CPFEM) is a powerful numerical simulation in the integrated computational materials engineering (ICME) toolboxes that relates microstructures to homogenized materials properties and establishes the structure-property linkages in computational materials science. However, to establish the predictive capability, one needs to calibrate the underlying constitutive model, verify the solution and validate the model prediction against experimental data. Bayesian optimization (BO) has stood out as a gradient-free efficient global optimization algorithm that is capable of calibrating constitutive models for CPFEM. In this paper, we apply a recently developed asynchronous parallel constrained BO algorithm to calibrate phenomenological constitutive models for stainless steel 304L, Tantalum, and Cantor high-entropy alloy.

READ FULL TEXT

page 9

page 12

page 14

page 16

research
04/24/2020

An active learning high-throughput microstructure calibration framework for solving inverse structure-process problems in materials informatics

Determining a process-structure-property relationship is the holy grail ...
research
07/01/2020

A finite element model updating method based on global optimization

Finite element model updating of a structure made of linear elastic mate...
research
04/20/2016

An algorithm for the optimization of finite element integration loops

We present an algorithm for the optimization of a class of finite elemen...
research
01/26/2023

The multifaceted nature of uncertainty in structure-property linkage with crystal plasticity finite element model

Uncertainty quantification (UQ) plays a critical role in verifying and v...
research
10/31/2021

An accurate, robust, and efficient finite element framework for anisotropic, nearly and fully incompressible elasticity

Fiber-reinforced soft biological tissues are typically modeled as hypere...
research
10/25/2019

Leveraging Legacy Data to Accelerate Materials Design via Preference Learning

Machine learning applications in materials science are often hampered by...
research
04/04/2023

Equivariant Networks for Porous Crystalline Materials

Efficiently predicting properties of porous crystalline materials has gr...

Please sign up or login with your details

Forgot password? Click here to reset