Effect of Feature Selection Using Recursive Feature Elimination on Tensile Strength Prediction Modeling of Low-Alloy Steel
Keywords:
Tensile strength, low-alloy steel, feature selection, Recursive Feature Elimination, Random ForestAbstract
In machine learning-based tensile strength prediction modeling of low-alloy steel, significant complexity often arises due to variations in chemical composition and heat treatment temperature. Therefore, this study applies feature selection using Recursive Feature Elimination (RFE) combined with the Random Forest algorithm to identify the most influential chemical elements and heat treatment temperature affecting the tensile strength of low-alloy steel. The RFE-Random Forest model was evaluated using different numbers of input features, and model performance was assessed using three evaluation metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²). The results indicate that the model incorporating seven input variables (C, Mn, Ni, Cr, Mo, V, and Temperature) achieved the best predictive performance, with an MAE of 20.333, an RMSE of 30.412, and an R-squared value of 0.945. External validation using unseen data demonstrated a significant improvement compared with the training stage, yielding an MAE of 13.472, an RMSE of 17.334, and an R-squared value of 0.978. These findings suggest that feature selection using RFE combined with Random Forest is a reliable approach for efficiently designing and predicting the mechanical properties of low-alloy steel.
References
[1] Dejene, N. D., & Lemu, H. G. (2023). Current Status and Challenges of Powder Bed Fusion-Based Metal Additive Manufacturing: literature review. Metals, 13(2), 424.
[2] Zhou, L., Miller, J., Vezza, J., Mayster, M., Raffay, M., Justice, Q., ... & Bernat, J. (2024). Additive Manufacturing: A Comprehensive Review. Sensors, 24(9), 2668.
[3] Giovagnoli, M., Silvi, G., Merlin, M., & Di Giovanni, M. T. (2021). Optimisation of Process Parameters for an Additively Manufactured Alsi10mg Alloy: Limitations of The Energy Density-Based Approach on Porosity and Mechanical Properties Estimation. Materials Science and Engineering: A, 802, 140613.
[4] Šolić, S., Sakač, I., Tropša, V., & Šercer, M. (2024). Influence of The Lpbf Process Parameters on The Porosity of The Alsi10mg Alloy. Tehnički glasnik, 18(1), 87-91.
[5] Calignano, F., Cattano, G., & Manfredi, D. (2018). Manufacturing of Thin Wall Structures in Alsi10mg Alloy by Laser Powder Bed Fusion Through Process Parameters. Journal of Materials Processing Technology, 255, 773-783.
[6] Mishra, A. K., & Kumar, A. (2024). Effect of Process Parameters on Melt Pool Characteristics and Solidification Process During Laser Powder Bed Fusion of AlSi10Mg Alloy. Lasers in Manufacturing and Materials Processing, 11(2), 260-283.
[7] Leni, D., Karudin, A., Abbas, M. R., Sharma, J. K., & Adriansyah, A. (2024). Optimizing Stainless Steel Tensile Strength Analysis: Through Data Exploration and Machine Learning Design with Streamlit. EUREKA: Physics and Engineering, (5), 73-88.
[8] Leni, D., Sumiati, R., Haris, H., Karudin, A., Kusuma, Y. P., & Afriyani, S. (2026, January). Application of Machine Learning Algorithms for Predicting Mechanical Properties of Stainless Steel. In AIP Conference Proceedings (Vol. 3337, No. 1, p. 040004). AIP Publishing LLC.
[9] Leni, D., Utami, L. P., Berli, A. U., Adriansyah, & Haris. (2025, January). Feature Selection with Information Gain in Machine Learning Modeling for Predicting Low-Alloy Steel Mechanical Properties. In AIP Conference Proceedings (Vol. 3223, No. 1, p. 020023). AIP Publishing LLC.
[10] Anjum, M., Khan, K., Ahmad, W., Ahmad, A., Amin, M. N., & Nafees, A. (2022). New Shapley Additive Explanations (Shap) Approach to Evaluate The Raw Materials Interactions of Steel-Fiber-Reinforced Concrete. Materials, 15(18), 6261.
[11] Wang, Z., Liu, H., Amin, M. N., Khan, K., Qadir, M. T., & Khan, S. A. (2024). Optimizing Machine Learning Techniques and Shapley Additive Explanations (Shap) Analysis for The Compressive Property of Self-Compacting Concrete. Materials Today Communications, 39, 108804.
[12] Luo, Q., Huang, N., Fu, T., Wang, J., Bartles, D. L., Simpson, T. W., & Beese, A. M. (2024). Dataset of Process-Structure-Property Feature Relationships for Alsi10mg Material Fabricated Using Laser Powder Bed Fusion Additive Manufacturing. Data in Brief, 53, 110130.
[13] Maimuzar, m., Hendra, h., Khan, S., Leni, D., & Islahuddin, i. (2024). Optimizing rediction of stainless steel mechanical properties with random forest: a comparison of feature selection methods. JURNAL POLIMESIN : Politeknik Negeri Lhokseumawe, 22(5), 498.
[14] Kwak, S., Kim, J., Ding, H., Xu, X., Chen, R., Guo, J., & Fu, H. (2022). Machine Learning Prediction of The Mechanical Properties of Γ-Tial Alloys Produced Using Random Forest Regression Model. Journal of Materials Research and Technology, 18, 520-530.
[15] Utami, L. P., Armijal, A., Leni, D., & Kasmar, A. F. (2025). Prediction of Aluminum Alloy Mechanical Properties Using Synthetic Data Generated by Generative Adversarial Networks. Jurnal Polimesin, 23(2), 159-168.
[16] Leni, D., Kesuma, D. S., Maimuzar, Haris, & Afriyani, S. (2024). Prediction of Mechanical Properties of Austenitic Stainless Steels with The Use of Synthetic Data Via Generative Adversarial Networks. Engineering Proceedings, 63(1), 4.
[17] Spierings, A. B., Herres, N., & Levy, G. (2011). Influence of The Particle Size Distribution on Surface Quality and Mechanical Properties in Am Steel Parts. Rapid Prototyping Journal, 17(3), 195–202. https://doi.org/10.1108/13552541111124770
[18] Read, N., Wang, W., Essa, K., & Attallah, M. M. (2015). Selective Laser Melting of Alsi10mg Alloy: Process Optimisation and Mechanical Properties Development. Materials & Design, 65, 417–424. https://doi.org/10.1016/j.matdes.2014.09.044
[19] Hyer, H., Zhou, L., Park, S., Gottsfritz, G., Benson, G., Tolentino, B., ... & Sohn, Y. (2020). Understanding The Laser Powder Bed Fusion of AlSi10Mg Alloy. Metallography, Microstructure, and Analysis, 9(4), 484-502.
[20] Wang, A., Wei, Q., Tang, Z., Oliveira, J. P., Leung, C. L. A., Ren, P., ... & Wang, H. (2024). Effects of Hatch Spacing on Pore Segregation and Mechanical Properties During Blue Laser Directed Energy Deposition of AlSi10Mg. Additive Manufacturing, 85, 104147.
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Copyright (c) 2026 ega Putri Utami, Sanny Ardhy, Meiki Eru Putra, Faisal Ananda, S.Sinatu Sadiyah
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