Reducing the dimensionality of the set of predictors for predicting the solubility of impurities in copper-based solid solutions - Shyiatyi, Vladislav (National Technical University "Kharkiv Polytechnic Institute", 2025)
This study investigates the use of dimensionality reduction techniques to improve the accuracy of impurity solubility prediction in copper-based alloys. Principal Component Analysis (PCA) and correlation based distance metrics are combined to identify the most influential predictors and classify solubility behaviour. The proposed approach offers a robust foundation for the development of predictive models in the field of alloy design.
Keywords: solid solution; copper alloys; solubility prediction; dimensionality reduction; PCA; impurity behaviour; the correlation metric