Pemilihan Algoritma Machine Learning Yang Optimal Untuk Prediksi Sifat Mekanik Aluminium

Abstract

<em>This study designs and compares optimal machine learning models to predict the mechanical properties of aluminum, including Yield Strength (YS) and Tensile Strength (TS), based on the percentage composition of aluminum's chemical elements. The machine learning modeling in this study has nine input variables consisting of aluminum chemical elements such as Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and two output or target variables consisting of YS and TS. Additionally, Heatmap correlation is used to observe the correlation between chemical elements and the mechanical properties of aluminum. Three machine learning algorithms, namely Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN), are compared in this study. The comparison of these algorithms shows that Random Forest (RF) outperforms the other algorithms in predicting YS with MAE of 11.44, RMSE of 14.282, and R value of 0.93. On the other hand, ANN performs better in predicting TS with MAE of 19.593, RMSE of 22.005, and R value of 0.947.</em>

Prediction of Mechanical Properties of Austenitic Stainless Steels with the Use of Synthetic Data via Generative Adversarial Networks

“…Each machine learning model is validated using cross-validation, a technique that allows the training data to be divided into several different subsets or folds. Iterations are performed on each subset to serve as testing data, while the other subsets are used as training data [13,14]. In the prediction modeling of austenitic stainless steel mechanical properties, cross-validation with a K value of 10 is used, where the data is divided into 10 different subsets or folds, and iterations are performed 10 times, selecting each subset alternately as the testing data and the others as the training data.…”

“…T results of the KS test for synthetic data compared to real data can be seen in Table 2. In this study, the Kolmogorov-Smirnov (KS) test was also conducted, which is a statistical method used to test the similarity of distributions between two groups of data or samples [13]. In the context of this research, the KS test is employed to compare the distribution of synthetic data with the distribution of real data.…”