Materials Design Toolkit

Summary

This framework provides the generic environment for materials design. The ICME (Integrating Computational Materials Engineering) approach is implemented for processing-structure-property correlation with a optimizer for material selection.

Description

Microstructure evolution can be simulated using the CALPHAD approach and phase based models using thermodynamics software and  models. The simulated microstructure is then evaluated by constitutive models and PyMKS. The optimization algorithms use the performance of the alloy to direct the search towards the high performance variable space. This Python based workflow is designed to accommodate the numerical models in various programming languages. 

Major Accomplishments

CASE STUDY: γ/γ′ Ni-based superalloy

Tools: CALPHAD type and structure-property-processing relationship software, genetic algorithm

Programming Language: Python, C, Fortran

The purpose of the case study was to demonstrate the selection of chemical composition and processing conditions of a γ/γ′ Ni_(1-x-y)Al_xCr_y superalloy  for high work to necking. Classic nucleation, growth, and coarsening models together with the CALPHAD approach are used to simulate the microstructure evolution during heat treatment. Because of the γ′ growth, the processing time is the determining factor for maximizing yield stress at service temperature. To evaluate the mechanical properties and work to necking of the microstructure, the mechanistic models, implemented in PyMKS, and a constitutive plastic deformation model are implemented. With this processing-structure-property correlation framework, the genetic algorithm directs the search until the target criteria are fulfilled and recommends the alloy composition and processing conditions.

CASE STUDY: High strain rate deformation of carbon steels

Tools: CALPHAD type software, image processing package, Basin-hopping algorithm

Programming Language: Python, C, Fortran

In manufacturing processes, workpiece materials are subjected to rapid heating, high loading rates and large plastic strains. Depending on the temperature involved, austenitic transformation significantly affects the mechanical behavior of carbon steels. To capture this processing-structure-properties relations for design purpose, we have a developed a workflow platform to integrate Materials Data Curation System (MDCS), model simulation, and software applications. This platform allows data to be easily transformed and used with other applications. For example, simple scripts allow the SEM micrographs to be access and analyzed by image processing software to the characterize microstructures for later use with the simulation models. CALPHAD-based phase equilibrium calculations are integrated with phase transformation models. These results are then integrated with the experimental data and constitutive models to predict time-dependent plastic deformation under rapid heating and loading. This flexible design framework enables the integration of experimental data and composition-dependent models to rapidly develop processing-structure-property relations.