Abstract
A new approach method has been studied for the efficient and accurate prediction of high-entropy alloys (HEAs) properties. The artificial neural network (ANN) algorithm was employed to predict the mechanical properties such as yield strength, microstructure, and elongation of the alloy by training from the mole fraction and post-process information that has an influence on the mechanical properties. The mean error rate of prediction for the yield strength was 19.6%. Microstructure predictions were consistent for all test data. On the other hand, the ANN model trained only with mole fraction data had a yield strength prediction error of 33.9%. Omission of post-process data caused a decrease in the accuracy. In addition, the prediction was performed with the lasso regression model in the same way. The mean error rate of the lasso model trained with only a mole fraction was 26.1%. The lasso model trained with a mole fraction and post-process data had a yield strength prediction error of 31.1%. The linear regression equation showed limitations, as the accuracy decreased as the number of independent variables increased. As there are more variables affecting metal properties, the ANN approach is more advantageous, and the more data there are, the more accuracy increases, making it possible to design HEAs alloys that are simpler and more efficient than conventional methods. This approach predicted HEAs properties using only mole fraction and post-processing information, without the need to use conventional physicochemical theories or perform derived complex calculations.
Highlights
High-entropy alloys (HEAs) are alloys that are formed by mixing equal or relatively large proportions of usually five or more elements
In this study, based on the artificial neural network (ANN) algorism, we propose an efficient framework for selecting the optimal component elements and post-process conditions in the most important step, HEA design
Figure shows the prediction results of model trained by mole fraction and Figure 2 shows the prediction results the of the model trained by mole fraction post-process information for eight test data
Summary
High-entropy alloys (HEAs) are alloys that are formed by mixing equal or relatively large proportions of usually five or more elements. HEAs systems are in contrast to traditional alloys, which contain just one or two primary constituent chemical species. There are thousands of combinations for experiments with all elements including mole fractions. It is impossible to carry out experiments in all cases. An empirical design through trial and error has been replaced by computer-based alloy designs. In terms of the formulation and accuracy of the predictive model, most of it comes from experimental data, which requires a significant amount of experimental input. HEAs have been generally known to require high entropy to obtain a stable phase of a single solid solution [1]
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