AM Process-Structure Modeling for Reliable Fatigue Prediction
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Solution Overview
Problem
Metal-based additive manufacturing (AM) processes lack reproducibility and reliability due to sensitivity to build conditions, and multiscale simulation methods face challenges in accurately modeling strain localization and efficient concurrent simulations.
Innovation Solution
An integrated process-structure-property modeling framework that includes a database and coupled models such as powder spreading, powder melting, grain growth, self-consistent clustering analysis (SCA), finite element method (FEM), and fatigue prediction models, which exchange information through an STL-format surface mesh representation to predict stress-strain responses and fatigue life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If metal-based additive manufacturing processes are used to achieve flexibility in part production, then adaptability is improved, but reliability deteriorates due to sensitivity to build conditions
Solution Approach 1:
The patent performs preliminary computational modeling and simulation of the additive manufacturing process before actual production. Process models predict temperature profiles, microstructure evolution, and residual stresses, while structure models simulate grain growth and phase transformations. This preliminary virtual experimentation allows optimization of build parameters and identification of potential quality issues before physical manufacturing, thereby improving reliability and reproducibility while maintaining process flexibility.
2Manufacturing precision
If integrated process-structure-property modeling framework is implemented to improve predictive accuracy, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex modeling framework into distinct modular components: process models (temperature field, phase transformation), structure models (microstructure evolution, grain growth), and property models (mechanical properties, fatigue life). Each module can be independently developed, validated, and adjusted. This segmentation reduces overall system complexity while maintaining high predictive accuracy, as each module focuses on specific physical phenomena without requiring full coupling of all processes.
Data Source
AI summary
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.