Table 2 The average and standard deviation of R2 score, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) for the prediction performance of mechanical properties using the machine learning models, where std denotes the standard deviation
Property | RF | GBT | ANN | SVR | KNN | |||||
|---|---|---|---|---|---|---|---|---|---|---|
R2 score | Mean | Std | Mean | Std | Mean | Std | Mean | Std | Mean | Std |
 Tensile strength | 0.917 | 0.025 | 0.922 | 0.023 | 0.887 | 0.045 | 0.899 | 0.034 | 0.889 | 0.037 |
 Yield strength | 0.878 | 0.043 | 0.889 | 0.040 | 0.839 | 0.055 | 0.863 | 0.047 | 0.862 | 0.045 |
 Elongation | 0.694 | 0.145 | 0.707 | 0.127 | 0.644 | 0.100 | 0.682 | 0.094 | 0.681 | 0.113 |
Error metrics | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE |
 Tensile strength (MPa) | 31.34 | 43.45 | 30.72 | 43.28 | 34.05 | 49.90 | 33.83 | 47.90 | 34.24 | 49.02 |
 Yield strength (MPa) | 36.71 | 53.19 | 36.64 | 52.72 | 40.42 | 58.96 | 38.30 | 56.56 | 37.80 | 56.94 |
 Elongation (%) | 2.92 | 4.24 | 2.87 | 4.06 | 3.46 | 4.60 | 3.22 | 4.40 | 3.26 | 4.45 |
