THE IMPLEMENTATION OF PANDAS PROFILING AS A TOOL FOR ANALYZING MECHANICAL PROPERTIES DATA OF NICKEL-BASED SUPERALLOYS BASED ON ALLOY CHEMICAL COMPOSITION

Desmarita Leni, Yuda Perdana Kusuma, Muchlisinalahuddin Muchlisinalahuddin, Ruzita Sumiati, Hendri Candra Mayana

Abstract


The purpose of this study is to evaluate the mechanical properties of nickel-based superalloys with variations in alloy chemical compositions using the Exploratory Data Analysis (EDA) method with the assistance of the pandas profiling library on Google Colab. In this study, data from 312 tensile tests of nickel-based superalloys were used as research samples, with alloy chemical compositions including carbon (C), manganese (Mn), silicon (Si), chromium (Cr), nickel (Ni), molybdenum (Mo), vanadium (V), nitrogen (N), niobium (Nb), cobalt (Co), tungsten (W), aluminum (Al), and titanium (Ti), as well as mechanical properties such as yield strength (YS), tensile strength (TS), and elongation (EL). The methodology used in this study was the EDA method with the assistance of the pandas profiling library on Google Colab, which enables the automatic creation of a dataset report, presenting information on various aspects such as data structure, descriptive statistics, correlation, distribution, and missing values. The results show that yield strength has a fairly high correlation with titanium (0.51), medium correlations with nickel (0.25), vanadium (0.2), and cobalt (0.2). Tensile strength in nickel-based superalloys has a fairly high correlation with yield strength (0.88), carbon (0.49), and cobalt (0.55), and medium correlations with titanium (0.25) and vanadium (0.25). Elongation in nickel-based superalloys has a negative and fairly high correlation with tensile strength (-0.62) and yield strength (-0.58). Some warnings for missing data and zero values in some variables were identified. These results indicate that the pandas profiling library can be used as a tool to analyze the data of mechanical properties of nickel-based superalloys quickly and easily, and provide clear information on data patterns, data structure, and correlation among data.


Keywords


Mechanical Properties; Nickel-Based Superalloys; Exploratory Data Analysis; Pandas Profiling

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References


Pollock, T. M., & Tin, S. (2006). Nickel-based superalloys for advanced turbine engines: chemistry, microstructure and properties. Journal of propulsion and power, 22(2), 361-374.

Yeh, A. C., Sato, A., Kobayashi, T., & Harada, H. (2008). On the creep and phase stability of advanced Ni-base single crystal superalloys. Materials Science and Engineering: A, 490(1-2), 445-451.

Mabruri, E. (2015). Peranan unsur refraktori didalam nickel-based superalloys: suatu review. Metalurgi, 26(2), 67-78.

Caron, P., & Khan, T. (1999). Evolution of Ni-based superalloys for single crystal gas turbine blade applications. Aerospace Science and Technology, 3(8), 513-523.

Wei, J., Chu, X., Sun, X. Y., Xu, K., Deng, H. X., Chen, J., ... & Lei, M. (2019). Machine learning in materials science. InfoMat, 1(3), 338-358.

Martinez, W. L., Martinez, A. R., & Solka, J. (2017). Exploratory data analysis with MATLAB. CRC Press.

Martinez, W. L., & Martinez, A. R. (2015). Computational statistics handbook with MATLAB (Vol. 22). CRC press.

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

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

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

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

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

Ward, L., Dunn, A., Faghaninia, A., Zimmermann, N. E., Bajaj, S., Wang, Q., ... & Jain, A. (2018). Matminer: An open source toolkit for materials data mining. Computational Materials Science, 152, 60-69.

Brugman, S. (2019). Pandas-profiling. https://github.com/pandas-profiling/pandas-profiling-data

Nguyen, Q. V., Simoff, S., Qian, Y., & Huang, M. L. (2016, September). Deep exploration of multidimensional data with linkable scatterplots. In Proceedings of the 9th International Symposium on Visual Information Communication and Interaction (pp. 43-50).

Allen, M., & Cervo, D. (2015). Multi-domain master data management: Advanced MDM and data governance in practice. Morgan Kaufmann.




DOI: http://dx.doi.org/10.22441/ijimeam.v4i3.19439

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