Automated Material Coefficient Extraction for TMF Simulations
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Solution Overview
Problem
Current methods for extracting material coefficients from thermo-mechanical fatigue (TMF) tests are inefficient, requiring manual trials and extensive optimization processes, often taking several weeks to derive coefficients for a single metal alloy, due to the large number of unknown variables involved.
Innovation Solution
The method defines and stores optimization variables that can be used to calculate material coefficients, reducing the number of variables needed for optimization, allowing for efficient and quick optimization using software like Ansys's DesignXplorer, which iteratively adjusts these variables to match experimental data within a desired error threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual trials and optimization software are used to extract material coefficients from experimental data, then the coefficients can be derived, but the process takes 8 to 12 weeks due to the large number of unknown variables
Solution Approach 1:
The patent segments the material coefficients into two distinct groups: optimization variables (a small set of 3-5 key parameters) and dependent coefficients (the remaining parameters that can be calculated from the optimization variables). This segmentation allows the optimization process to focus only on the critical variables while automatically deriving the rest, reducing the extraction time from 8-12 weeks to a fraction of that time while maintaining accuracy.
Solution Approach 2:
The patent extracts and isolates the most influential parameters from the complete set of material coefficients, creating a reduced set of optimization variables. By taking out only the essential variables that have the greatest impact on material behavior, the optimization process becomes computationally efficient and rapid, yet still produces accurate results for all coefficients.
2Reliability
If all unknown coefficients are used directly in the optimization algorithm, then complete material model accuracy is maintained, but the optimization becomes computationally infeasible or fails due to too many variables
Solution Approach 1:
The patent divides the complete set of material coefficients into two segments: optimization variables (3-5 key parameters) and dependent coefficients (calculated from the optimization variables). This segmentation reduces the optimization problem from handling dozens of variables to managing only a handful, making the optimization computationally feasible while preserving the ability to represent complex material behavior through the relationship between optimization variables and dependent coefficients.
Solution Approach 2:
The patent extracts the essential controlling parameters from the full set of material coefficients, creating a minimized optimization variable set. By taking out only the critical variables that govern material response, the optimization algorithm becomes tractable and reliable, while the complete material model accuracy is maintained through the mathematical relationships that connect optimization variables to all other coefficients.
3Measurement precision
If manual trials and plotting methods are used to extract material coefficients, then engineers can derive coefficients, but the process requires extensive manual effort and consultant involvement
Solution Approach 1:
The patent implements a self-service automated system that performs the entire coefficient extraction process without requiring manual trials, plotting, or consultant intervention. The system automatically identifies optimization variables, performs the optimization against experimental data, calculates dependent coefficients, and validates results. This automation maintains extraction accuracy while dramatically improving ease of operation, allowing material engineers to independently complete the process in hours rather than weeks.
Solution Approach 2:
The patent replaces the manual mechanical process of trial-and-error coefficient extraction with an automated computational system. Instead of engineers manually adjusting parameters and plotting results, the system uses optimization algorithms to automatically determine optimal values. This substitution maintains scientific rigor and accuracy while eliminating the need for manual effort and specialized consultant expertise.
Data Source
AI summary
Systems and methods for deriving material coefficient values from physical measurements of physical objects are described. These physical measurements can provide material test data. In one embodiment of a method described herein, the material test data can be used to calculate a first subset of material coefficients for use in one or more material models from material test data; the method can define a set of optimization parameters and define a set of relationships between a second subset of the material coefficients and the set of optimization parameters such that values of the material coefficients in the second subset of material coefficients can be calculated from the set of optimization parameters, wherein the first subset and the second subset include all of the material coefficients in the one or more material models and the set of optimization parameters have fewer parameters than a total number of coefficients in the first subset and the second subset; then the method can calculate optimum values of the optimization parameters to yield an optimum fit of the one or more models to the material test data, the optimum fit based on optimum material coefficients calculated from the calculated optimum values of the optimization parameters.