研究目的
To develop a fast regression-based model for calculating the sensitivity of alloy properties to variations in element concentrations, enabling robust optimization in alloy-by-design processes for nickel-base superalloys.
研究成果
A fast regression-based model for property sensitivity has been developed and integrated into the optimization workflow, allowing for online sensitivity optimization with minimal computational overhead. This enables the selection of robust alloy compositions that are less affected by manufacturing inaccuracies, with an average prediction error of about 4%. The model can be extended to include database inaccuracies and future work should incorporate sensitivity calculations for third phases.
研究不足
The model assumes nearly linear behavior of functions within the uncertainty range and does not account for interactions between design variables in sensitivity calculations. It also relies on the accuracy of CALPHAD databases, which may have uncertainties, especially for TCP phases. The sensitivity calculation for third phases is not included and requires post-optimization processing.
1:Experimental Design and Method Selection:
The study uses a regression-based model derived from CALPHAD calculations to predict property sensitivity. It involves statistical regression analysis with stepwise variable selection to identify the most appropriate model for sensitivity prediction.
2:Sample Selection and Data Sources:
Approximately 50,000 data points were calculated within defined concentration and uncertainty ranges for nickel-base superalloys, using the TTNi8 database for CALPHAD calculations.
3:List of Experimental Equipment and Materials:
Software tools include MATLAB (version 2016a 64-bit) for regression analysis, Thermo-Calc (version 2017b) with TC-API for thermodynamic calculations, and the in-house tool MultOpt++ for optimization. No physical equipment is mentioned.
4:Experimental Procedures and Operational Workflow:
The process involves pre-calculating regression coefficients for property distributions, applying these in multi-objective optimization using evolutionary algorithms, and validating the model accuracy against recalculated sensitivities.
5:Data Analysis Methods:
Statistical methods include stepwise regression, hypothesis testing (e.g., Shapiro-Wilk test), and goodness-of-fit measures like AIC, RMSE, and R2 to evaluate model performance.
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