Analysis of Mechanical Property Prediction of Cast Copper Alloys Based on Chemical Composition Using Machine Learning
DOI:
https://doi.org/10.67795/jesai.v1i1.10Keywords:
Cast Copper Alloys; Machine Learning; Decision Tree; Random Forest; Mechanical Properties PredictionAbstract
Cast copper alloys are widely used in industrial applications because of their high electrical and thermal conductivity, excellent corrosion resistance, and favorable mechanical properties. However, mechanical properties such as Ultimate Tensile Strength (UTS) and Yield Strength (YS) are strongly influenced by variations in chemical composition, making conventional testing time-consuming and costly. This study aims to analyze the prediction of the mechanical properties of cast copper alloys based on their chemical composition using machine learning. The dataset consisted of 100 cast copper alloy samples obtained from MakeItFrom.com, with 22 chemical elements as input variables and UTS and YS as output variables. The research process included data preprocessing, correlation analysis, hyperparameter optimization, and model development using the Decision Tree and Random Forest algorithms. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), the coefficient of determination (R²), and 5-Fold Cross Validation under three training-testing data split scenarios (60:40, 70:30, and 80:20). The results showed that the Decision Tree algorithm achieved better predictive performance than Random Forest. The best model, obtained using the 70:30 data split, achieved RMSE values of 95.618 and 62.614, MAE
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Copyright (c) 2026 Ikhwanul Yogi Arvandy, Desmarita Leni, Jana Hafiza
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