Closed-Loop FEM Simulation for Non-Equilibrium Process History
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Solution Overview
Problem
Current manufacturing process model simulations are limited by their reliance on single tabular databases of equilibrium thermo-mechanical properties, failing to account for non-equilibrium effects and thermo-mechanical history, leading to inefficiencies and high costs due to trial-and-error experimentation.
Innovation Solution
A method that arranges a finite element method (FEM) simulation model, a microstructure model, and a material model in a closed loop, performing iterative simulations to determine thermomechanical parameters and predict material properties based on process parameters and previous predicted material properties, effectively accounting for time-dependent microstructure history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current process model simulations use a single tabular look-up database of equilibrium thermo-mechanical properties, then the simulation model is simple, but it cannot account for non-equilibrium effects and thermo-mechanical history
Solution Approach 1:
The patent embeds multiple models within each other: the microstructure model is nested within the material model, which is in turn nested within the FEM simulation model. This hierarchical nesting allows the system to capture non-equilibrium effects and thermo-mechanical history through the microstructure model while maintaining the overall structure of a standard FEM simulation, thus improving accuracy without excessive complexity
Solution Approach 2:
The patent introduces a microstructure model as an intermediary between the FEM simulation model and the material model. This intermediary captures the thermo-mechanical history and non-equilibrium effects, translating complex microstructural evolution into effective material properties that the FEM model can use, thereby resolving the contradiction between accuracy and complexity
2Productivity
If trial-and-error experimentation is used to determine process parameters, then the simulation model remains simple, but it leads to high costs, significant material requirements, scrap, and extended development lead-time
Solution Approach 1:
The patent performs preliminary computational simulations using the integrated FEM-microstructure-material model to predict optimal process parameters before actual manufacturing. This preliminary virtual experimentation identifies the best parameters in advance, reducing or eliminating the need for costly trial-and-error physical experimentation, thereby improving productivity and reducing material scrap
Solution Approach 2:
The patent creates a virtual copy of the manufacturing process through computational simulation that incorporates microstructure evolution. This virtual model allows testing and optimization of process parameters in silico rather than through physical trial-and-error experimentation, reducing material consumption and accelerating development
Data Source
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Figure 2A~2B
Figure 2C
AI summary
A method (100) for optimizing a manufacturing process includes arranging a finite element method (FEM) simulation model (206), a microstructure model (208), and a material model (210) in a closed loop. The method (100) includes performing an iterative simulation process (200) to determine a simulated output (220) of the manufacturing process. The iterative simulation process (200) includes performing a plurality of iterations (212). Each of the plurality of iterations (212) includes the steps of: determining, by the FEM simulation model (206), a plurality of thermomechanical parameters (214) based on a plurality of process parameters (204) and a plurality of previous predicted material properties (218P); determining, by the microstructure model (208), a predicted change in a microstructure (216) based on the plurality of thermomechanical parameters (214); and determining, by the material model (210), a plurality of current predicted material properties (218C) based on the predicted change in the microstructure (216).