Evaluasi sifat mekanik baja paduan rendah bedasarkan komposisi kimia dan suhu perlakuan panas menggunakan teknik exploratory data analysis (EDA)

D. Leni, F. Earnestly, R. Sumiati, A. Adriansyah, Y.P. Kusuma


This research aims to evaluate the relationship between the chemical composition of low alloy steel, temperature, and the mechanical properties of low alloy steel using Exploratory Data Analysis (EDA) techniques. The low alloy steel dataset is visualized using a correlation heat map, which shows a relationship between the mechanical properties of low alloy steel and its chemical composition and heat treatment temperature. Based on the results of the correlation heat map, an evaluation is carried out using scatterplots. The visualization results with scatterplots show a trend line indicating a linear relationship between YS and the elements V, Ni, and Mn, as well as a positive relationship between TS and V. In addition, there are determination coefficients (R²) that show how well the trend line can explain the variation of the data. The values obtained for V are 0.405, Ni is 0.226, Mn is 0.159, and Mo is 0.130, while El and RA have a positive correlation with temperature with R²values of 0.166 and 0.320, respectively. It can be concluded that the evaluation results using scatterplots and R² show that variations in chemical composition and heat treatment temperature affect the mechanical properties of low alloy steel. The correlations that occur between these variables can help in determining the pattern of the relationship and evaluating how well the trend line can explain the variation of the data. The use of correlation heat maps and scatter plots can help in decision-making and developing low-alloy steel materials that meet specific needs.


Mechanical properties; low alloy steel; chemical composition; Exploratory Data Analysis (EDA) techniques

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Avazkonandeh, Gharavol, M.H., Haddad, Sabzevar, .M., Haerian, A, Effect of copper content on the microstructure and mechanical properties of multipass MMA, low alloy steel weld metal deposits. Materials & Design, 30(6), 1902-1912, 2009.

Aziz, E.M., Kodur, V.K, Effect of temperature and cooling regime on mechanical properties of high‐strength low‐alloy steel. Fire and Materials, 40(7), 926-939, 2016.

Chicco, D., Warrens, M.J., Jurman, G, The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623, 2021.

Frista, G., Notonegoro, H.A., Fachrudin, H.G, Peningkatan Sifat Mekanik AISI 4130 Low Alloy Steel Melalui Perlakuan Panas. FLYWHEEL: Jurnal Teknik Mesin Untirta, 2(1), 2017.

Flumignan, D.L., Anaia, G.C., De O. Ferreira, F., Tininis, A.G., De Oliveira, J.E, Screening brazilian automotive gasoline quality through quantification of saturated hydrocarbons and anhydrous ethanol by gas chromatography and exploratory data analysis. Chromatographia, 65, 617-623, 2007.

Goritskii, V.M., Shneiderov, G.R., Guseva, I.A, Effect of chemical composition and structure on mechanical properties of low-alloy weldable steels after thermo mechanical treatment. Metallurgist, 60, 511-518, 2016.

Jansen, F.E., Kelkar, M.G, Exploratory data analysis of production data. In Permian Basin Oil and Gas Recovery Conference. OnePetro, 1996.

Kürzl, H., Exploratory data analysis: recent advances for the interpretation of geochemical data. Journal of Geochemical Exploration, 30(1-3), 309-322, 1988.

Kumar, M., Kumar, A., Palaparthy, V.S,, Soil sensors-based prediction system for plant diseases using exploratory data analysis and machine learning. IEEE Sensors Journal, 21(16), 17455-17468, 2020.

Morini, A.A., Ribeiro, M.J., Hotza, D, Early-stage materials selection based on embodied energy and carbon footprint. Materials & Design, 178, 107861, 2019.

Merayo F.D., Rodríguez-Prieto, A., Camacho, A.M., Prediction of the bilinear stress-strain curve of aluminum alloys using artificial intelligence and big data. Metals, 10(7), 904, 2020.

Martinez, W.L., Martinez, A.R., Solka, J., Exploratory data analysis with MATLAB. Chapman and Hall/CRC, 2017.

Martinez, W.L., Martinez, A.R, Computational statistics handbook with MATLAB. Chapman and Hall/CRC. 2001.

Odeshi, A.G., Bassim, M.N., Al-Ameeri, S, Effect of heat treatment on adiabatic shear bands in a high-strength low alloy steel. Materials Science and Engineering, 419(1-2), 69-75, 2006.

Ogunsina, K., Bilionis, I., DeLaurentis, D., Exploratory data analysis for airline disruption management. Machine Learning with Applications, 6, 100102, 2021.

Wei, J., Sun, G., Zhao, L., Yang, X., Liu, X., Lin, D., Ma, X, Analysis of hair cortisol level in first-episodic and recurrent female patients with depression compared to healthy controls. Journal of affective disorders, 175, 299-302, 2015.

Wang, Z., Hui, W., Chen, Z., Zhang, Y., Zhao, X, Effect of vanadium on microstructure and mechanical properties of bainitic forging steel. Materials Science and Engineering, 771, 138653, 2020.

Zou, K.H., Tuncali, K.., Silverman, S.G., Correlation and simple linear regression. Radiology, 227(3), 617-628, 2003.

DOI: https://doi.org/10.29303/dtm.v13i1.624


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