Integrated process-structure-property modeling frameworks and methods for design optimization and/or performance prediction of material systems and applications of same
Inventors
Liu, Wing Kam • Gao, Jiaying • Yu, Cheng • Kafka, Orion L.
Assignees
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Publication Number
US-12572719-B2
Publication Date
2026-03-10
Expiration Date
Abstract
An integrated process-structure-property modeling framework for design optimization and/or performance prediction of a material system includes 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.
Core Innovation
The invention provides a computer implemented system for integrated process-structure-property modeling for design optimization and/or performance prediction of a material system. The system includes one or more processors, non-transitory computer readable memory storing executable instructions, and a database communicatively coupled to the processors. Executing the instructions causes the system to perform operations that model additive manufacturing process physics, resulting microstructural evolution, and mechanical property outcomes in a sequential workflow.
The system executes a powder spreading model using a discrete element method (DEM) to generate a representation of a powder bed corresponding to a physical additive manufacturing process, storing the representation in the database as a stereolithography (STL) or voxel surface-mesh file. It then executes a thermal-fluid flow model using the STL or voxel file to simulate a powder melting process to predict voids and a temperature profile in each volume of interest, and writes the voids and temperature profile information to the database in a flat file format.
Next, a cellular automaton (CA) model retrieves the voids and temperature profile from the database to simulate grain growth based on the temperature profile, and stores grain growth information in the database. Finally, the system executes a reduced-order micromechanics model that retrieves grain growth and voids information from the database to predict mechanical properties and fatigue resistance of resultant structures by resolving the voids and grains.
The models are coupled through the database so that output data of one model automatically serves as input data for a subsequent model, thereby establishing a sequentially coupled simulation of the physical additive manufacturing process. This coordinated coupling is positioned as a technical improvement in computer implemented simulation accuracy and computational efficiency, and as support for additive manufacturing process design.
Claims Coverage
The independent claim is clm-00001. It defines a sequentially coupled, database-mediated chain of four models (DEM powder spreading, thermal-fluid melting, CA grain growth, and reduced-order micromechanics) to predict mechanical properties and fatigue resistance, and to output material-performance metrics and optimized process parameters.
Database coupled sequentially coupled simulation
Models are coupled through the database such that output data of one model automatically serves as input data for a subsequent model, thereby establishing a sequentially coupled simulation of the physical additive manufacturing process.
Powder spreading to melting to voids and temperature profile
A powder spreading model using a discrete element method (DEM) generates a representation of a powder bed stored in the database as a stereolithography (STL) or voxel surface-mesh file, and a thermal-fluid flow model uses the STL or voxel file to simulate powder melting to predict voids and a temperature profile in each volume of interest, writing the voids and temperature profile to the database in a flat file format.
Cellular automaton grain growth from temperature profile and voids
A cellular automaton (CA) model retrieves the voids and temperature profile from the database to simulate grain growth based on the temperature profile, and stores grain growth information in the database.
Reduced-order micromechanics for mechanical properties and fatigue resistance
A reduced-order micromechanics model retrieves grain growth and voids information from the database to predict mechanical properties and fatigue resistance of resultant structures by resolving the voids and grains.
Performance metrics and optimized process parameters output
The system outputs material-performance metrics and optimized process parameters.
Claim clm-00001 centers on a database-coupled sequential workflow that converts DEM powder-bed representations into STL/voxel inputs for thermal-fluid melting, uses database-persisted voids and temperature profiles to drive CA grain growth, and then feeds grain growth plus voids into a reduced-order micromechanics model to predict mechanical properties and fatigue resistance.
Stated Advantages
Provides a technical improvement in computer implemented simulation accuracy.
Provides a technical improvement in computational efficiency.
Provides a technical improvement in additive manufacturing process design.
Supports additive manufacturing process design by outputting optimized process parameters.
Documented Applications
Design optimization and/or performance prediction of a material system using integrated process-structure-property modeling for a physical additive manufacturing process.
Selective electron beam or laser powder bed additive manufacturing of a metallic material.
Predicting mechanical properties and fatigue resistance of resultant structures produced by an additive manufacturing process, based on resolved voids and grains.
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