Integrated process-structure-property modeling frameworks and methods for design optimization and/or performance prediction of material systems and applications of same

A method of integrated process-structure-property modeling for design optimization and/or performance prediction of a material system, comprising:

• exchanging geometry information of a powder bed between a powder spreading model and a powder melting model using a stereolithography (STL)-format surface mesh representation;
• predicting a temperature profile in each volume of interest (VOI) by the powder melting model with a powder melting simulation;
• writing the temperature profile to the database in a flat file format;
• and setting a void space to an ambient temperature to distinguish said void space from a dense material;
• accessing the temperature profile and void information from the database and predicting grain information by a grain growth model based on the temperature profile and void information;
• writing the grain information to the database along with the voids;
• identifying the dense material and the voids on the temperature profile, thereby including the voids and free surfaces in the grain growth model;
• predicting stress-strain responses by the SCA and/or FEM models based on the grain and void information provided at each point via the database;
• and writing the stress-strain responses to the database;
• and
• accessing the stress-strain responses at each point via the database and computing a non-local potency estimate by a fatigue prediction model based on the stress-strain responses, wherein the fatigue life of the highest potency location is computed to provide a scalar material response metric.

An integrated process-structure-property modeling framework for design optimization and/or performance prediction of a material system, comprising

• a powder spreading model using a discrete element method (DEM) to generate a powder bed;
• a thermal-fluid flow model of the powder melting process to predict voids and temperature profile;
• a cellular automaton (CA) model to simulate grain growth based on the temperature profile;
• and
• a reduced-order micromechanics model to predict mechanical properties and fatigue resistance of resultant structures by resolving the voids and grains.

A method for implementation of multiresolution continuum theory (MCT), comprising:

• decomposing deformation across different microstructural length scales, by an MCT model;
• wherein the macroscale deformation is described by conventional degrees of freedom and extra degrees of freedom are introduced to describe the deformation at a microscale;
• employing a modified homogenization-based Gurson (GTN) model at the macroscale to result in equations for a void volume fraction, an effective plastic strain rate, and a matrix flow stress, wherein an additive decomposition of a rate of deformation tensor into elastic and plastic parts allows for description of an objective rate of Cauchy stress;
• solving the resulting equations at each scale using an iterative Newton-Raphson scheme to update the void volume fraction, the effective plastic strain rate, and the matrix flow stress based on a new deformation tensor, wherein the homogenized void growth behavior is taken to represent the response of the primary population of voids, including those initiated at inclusions, as the material is deformed;
• and
• constructing a plastic potential using microstress and microstress couple at the microscale, wherein a deviatoric strain rate is defined in terms of a relative deformation between the microscale and the macroscale, and solving equations of the plastic potential to obtain stress-strain profiles.

A method of data-driven mechanistic modeling of a system for predicting high cycle fatigue (HCF) crack incubation life, comprising:

• generating a representation of the system with matrix, inclusion and void, wherein the representation comprises microstructure volume elements (MVE) that are of building blocks of the system;
• obtaining strain contours in the matrix from a linear elastic analysis over a predefined set of boundary conditions;
• generating clusters from a strain solution by assigning voxels with similar strain concentration tensor within the representation of the material system to one of the clusters with a unique ID, using k-means clustering;
• computing a plastic shear strain field of the material system by solving the Lippmann-Schwinger equation using the generated clusters with a crystal plasticity (CP) model;
• and
• predicting the fatigue crack incubation life of the system using a fatigue indicating parameter (FIP) based on the plastic shear strain field.