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Article

Explainable AI and Feature Engineering for Machine-Learning-Driven Predictions of the Properties of Cu-Cr-Zr Alloys: A Hyperparameter Tuning and Model Stacking Approach

1
Computer Science Department, Faculty of Computers and Information, Suez University, Suez P.O. Box 43221, Egypt
2
Department of Mechanical Engineering, Faculty of Engineering, Suez University, Suez P.O. Box 43221, Egypt
3
Department of Mechanical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
4
Central Metallurgical Research & Development Institute (CMRDI), El-Tibbin-Helwan, Helwan 11421, Egypt
*
Authors to whom correspondence should be addressed.
Processes 2025, 13(5), 1451; https://doi.org/10.3390/pr13051451
Submission received: 2 April 2025 / Revised: 28 April 2025 / Accepted: 6 May 2025 / Published: 9 May 2025
(This article belongs to the Special Issue Heat Processing, Surface and Coatings Technology of Metal Materials)

Abstract

High-performance copper alloys are crucial for integrated circuit lead frames due to their high density, multifunctionality, and low cost. High-performance copper alloys typically address the competing issues of high strength and high electrical conductivity through alloying and processing control methods. However, the traditional methods for developing these alloys are time-consuming, expensive, and complex processes. This study utilizes Explainable AI by employing machine learning (ML) and deep learning (DL) techniques to predict the hardness (HRC) and electrical conductivity (mS/m) based on the alloy composition, including Cr, Zr, Ce, and La, and the processing parameters, namely the aging time, of Cu-Cr-Zr alloys. A comprehensive dataset of 47 experimental Cu-Cr-Zr alloy samples, derived from prior experimental studies, was analyzed using feature engineering, correlation analysis, and explainability methods such as SHapley Additive exPlanations (SHAP). Various ML models, including ensemble methods like XGBoost, CatBoost, and AdaBoost, were evaluated for their predictive performance. The feature importance analysis revealed that the aging time and Zr content significantly influence the hardness, followed by Ce content, while Cr and La contents reveal a weak contribution to hardness values. Electrical conductivity is predominantly controlled by aging time, with a weak negative influence of the alloying elements. These findings align well with metallurgical principles, where microstructural refinement and precipitation behavior dictate the hardness and conductivity of Cu-Cr-Zr alloys. Hyperparameter tuning and model stacking further enhanced the predictive accuracy, with the final stacked models achieving R2 scores of 0.8762 for hardness within a training time of 1.739 s and 0.8132 for electrical conductivity within a training time of 1.091 s. These findings demonstrate the effectiveness of ML-driven approaches in material property predictions, providing valuable insights for material design and property processing parameter optimization.
Keywords: explainable AI; machine learning; hardness; electrical conductivity; Cu-Cr-Zr-alloys; feature engineering; hyperparameter tuning optimization; ensemble learning; model stacking explainable AI; machine learning; hardness; electrical conductivity; Cu-Cr-Zr-alloys; feature engineering; hyperparameter tuning optimization; ensemble learning; model stacking

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MDPI and ACS Style

Atiea, M.A.; Reda, R.; Ataya, S.; Ibrahim, M. Explainable AI and Feature Engineering for Machine-Learning-Driven Predictions of the Properties of Cu-Cr-Zr Alloys: A Hyperparameter Tuning and Model Stacking Approach. Processes 2025, 13, 1451. https://doi.org/10.3390/pr13051451

AMA Style

Atiea MA, Reda R, Ataya S, Ibrahim M. Explainable AI and Feature Engineering for Machine-Learning-Driven Predictions of the Properties of Cu-Cr-Zr Alloys: A Hyperparameter Tuning and Model Stacking Approach. Processes. 2025; 13(5):1451. https://doi.org/10.3390/pr13051451

Chicago/Turabian Style

Atiea, Mohammed A., Reham Reda, Sabbah Ataya, and Mervat Ibrahim. 2025. "Explainable AI and Feature Engineering for Machine-Learning-Driven Predictions of the Properties of Cu-Cr-Zr Alloys: A Hyperparameter Tuning and Model Stacking Approach" Processes 13, no. 5: 1451. https://doi.org/10.3390/pr13051451

APA Style

Atiea, M. A., Reda, R., Ataya, S., & Ibrahim, M. (2025). Explainable AI and Feature Engineering for Machine-Learning-Driven Predictions of the Properties of Cu-Cr-Zr Alloys: A Hyperparameter Tuning and Model Stacking Approach. Processes, 13(5), 1451. https://doi.org/10.3390/pr13051451

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