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Artificial Neural Network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium

DOI: 10.4236/ijnm.2024.111001, PP. 1-19

Keywords: Al-5%Mg Alloy, Neodymium, Artificial Neural Network, Fuzzy Logic, Average Grain Size and Mechanical Properties

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Abstract:

In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).

References

[1]  Zhang, H. and Zhang, B. (2019) Effects of Ag on the Microstructures and Mechanical Properties of Al-Mg Alloys. In: Chesonis, C., Ed., Light Metals 2019, Springer, Berlin, 493-497.
https://doi.org/10.1007/978-3-030-05864-7_63
[2]  Kaibyshev, R., Musin, F., Lesuer, D. and Nieh, T. (2003) Superplastic Behavior of an Al-Mg Alloy at Elevated Temperatures. Materials Science and Engineering: A, 342, 169-177.
https://doi.org/10.1016/S0921-5093(02)00276-9
[3]  Zhou, H., Qian, Z., Zhou, M., Liu, X., Li, Y. and Zhang, X. (2020) Synergistic Balance of Strength and Corrosion Resistance in Al-Mg-Er Alloys. Acta Metallurgica Sinica (English Letters), 33, 659-670.
https://doi.org/10.1007/s40195-020-01007-1
[4]  Zhang, X., Wang, Z., Zhou, Z. and Xu, J. (2017) Influence of Rare Earth (Ce and La) Addition on the Performance of Al-3.0 wt%Mg Alloy. Journal of Wuhan University of Technology-Materials Science Edition, 32, 611-618.
https://doi.org/10.1007/s11595-017-1642-6
[5]  Song, M., Wu, Z. and He, Y. (2008) Effects of Yb on the Mechanical Properties and Microstructures of an Al-Mg Alloy. Materials Science and Engineering: A, 497, 519-523.
https://doi.org/10.1016/j.msea.2008.07.020
[6]  Wang, X., Chen, G., Li, B., Wu, L. and Jiang, D. (2010) Effects of Sc, Zr and Ti on the Microstructure and Properties of Al Alloys with High Mg Content. Rare Metals, 29, 66-71.
https://doi.org/10.1007/s12598-010-0012-8
[7]  Zhou, S., Zhang, Z., Li, M., Pan, D., Su, H., Du, X. and Wu, Y. (2016) Effect of Sc on Microstructure and Mechanical Properties of As-Cast Al-Mg Alloys. Materials & Design, 90, 1077-1084.
https://doi.org/10.1016/j.matdes.2015.10.132
[8]  Hu, Z., Ruan, X. and Yan, H. (2015) Effects of Neodymium Addition on Microstructure and Mechanical Properties of Near-Eutectic Al-12Si Alloys. Transactions of Nonferrous Metals Society of China, 25, 3877-3885.
https://doi.org/10.1016/S1003-6326(15)64035-3
[9]  Anukwonke, M.C., Chibueze, I.G. and Nnuka, E.E. (2021) A Fuzzy Logic Approach for Modeling and Prediction of Mechanical Properties of Al-5%Magnesium Alloy Doped with Nickel. International Journal of Innovative Engineering, Technology and Science, 4.
https://ijiets.coou.edu.ng/paper/a-fuzzy-logic-approach-for-modeling-and-prediction-of-mechanical-properties-of-al-5mg-doped-with-nickel/
[10]  Barzani, M.M., Zalnezhad, E., Sarhan, A.A., Farahany, S. and Ramesh, S. (2015) Fuzzy Logic Based Model for Predicting Surface Roughness of Machined Al-Si-Cu-Fe Die Casting Alloy Using Different Additives-Turning. Measurement, 61, 150-161.
https://doi.org/10.1016/j.measurement.2014.10.003
[11]  Rahman, F., Raheel, M. and Khan, R. (2019) Fuzzy Logic Model for Investigating the Effect of Steel Fibers on Mechanical Properties of Concrete. SN Applied Sciences, 1, Article No. 1205.
https://doi.org/10.1007/s42452-019-1226-5
[12]  Chibueze, I.G., Atuanya, C.U., Nwobi-Okoye, C.C. and Obele, C.M. (2021) Optimization and Modeling the Mechanical Properties of Bagasse and Luffer Cylindrica Fiber Reinforced Epoxy Polymer Hybrid Composite Using Taguchi Design and Fuzzy Logic. International Journal of Innovative Engineering, Technology and science, 4.
https://ijiets.coou.edu.ng/paper/optimization-and-modeling-the-mechanical-properties-of-bagasse-and-luffer-cylindrica-fiber-reinforced-epoxy-polymer-hybrid-composite-using-taguchi-design-and-fuzzy-logic/
[13]  Gencel, O., Brostow, W., del Coz Diaz, J.J., Martínez-Barrera, G. and Beycioglu, A. (2013) Effects of Elevated Temperatures on Mechanical Properties of Concrete Containing Haematite Evaluated Using Fuzzy Logic Model. Materials Research Innovations, 17, 382-391.
https://doi.org/10.1179/1433075X12Y.0000000070
[14]  Deng, Z., Yin, H., Jiang, X., Zhang, C., Zhang, G., Xu, B. and Qu, X. (2020) Machine-Learning-Assisted Prediction of the Mechanical Properties of Cu-Al Alloy. International Journal of Minerals, Metallurgy and Materials, 27, 362-373.
https://doi.org/10.1007/s12613-019-1894-6
[15]  Khalaj, G., Azimzadegan, T., Khoeini, M. and Etaat, M. (2012) Artificial Neural Networks Application to Predict the Ultimate Tensile Strength of X70 Pipeline Steels. Neural Computing and Applications, 23, 2301-2308.
https://doi.org/10.1007/s00521-012-1182-0
[16]  Mahalle, G., Salunke, O., Kotkunde, N., Gupta, A.K. and Singh, S.K. (2019) Neural Network Modeling for Anisotropic Mechanical Properties and Work Hardening Behavior of Inconel 718 Alloy at Elevated Temperatures. Journal of Materials Research and Technology, 8, 2130-2140.
https://doi.org/10.1016/j.jmrt.2019.01.019
[17]  Singh, S.K., Mahesh, K. and Gupta, A.K. (2010) Prediction of Mechanical Properties of Extra Deep Drawn Steel in Blue Brittle Region Using Artificial Neural Network. Materials & Design (1980-2015), 31, 2288-2295.
https://doi.org/10.1016/j.matdes.2009.12.012
[18]  Parvizi, S., Hafizpour, H.R., Sadrnezhaad, S.K., Akhondzadeh, A. and Abbasi Gharacheh, M. (2011) Neural Network Prediction of Mechanical Properties of Porous NiTi Shape Memory Alloy. Powder Metallurgy, 54, 450-454.
https://doi.org/10.1179/003258910X12827272082588
[19]  Shabani, M.O. and Mazahery, A. (2011) The ANN Application in FEM Modeling of Mechanical Properties of Al-Si Alloy. Applied Mathematical Modelling, 35, 5707-5713.
https://doi.org/10.1016/j.apm.2011.05.008
[20]  Sterjovski, Z., Nolan, D., Carpenter, K.R., Dunne, D.P. and Norrish, J. (2005) Artificial Neural Networks for Modelling the Mechanical Properties of Steels in Various Applications. Journal of Materials Processing Technology, 170, 536-544.
https://doi.org/10.1016/j.jmatprotec.2005.05.040
[21]  Haghayeshi, R. and Laurentiu, N. (2012) An Investigation on Grain Refinement of Al Alloys through Liquid Shearing. Conference: Materials Science and Technology (MS&T), Pittsburgh, 7-11 October 2012.
[22]  Nayeb-Hashemi, A. (1988) Phase Diagrams of Binary Magnesium Alloys. ASM International, Metals Park, 370.
[23]  Čelko, L., Klakurková, L. and Švejcar, J. (2010) Diffusion in Al-Ni and Al-NiCr Interfaces at Moderate Temperatures. Defect and Diffusion Forum, 297-301, 771-777.
https://doi.org/10.4028/www.scientific.net/DDF.297-301.771
[24]  Akoglu, H. (2018) User’s Guide to Correlation Coefficients. Turkish Journal of Emergency Medicine, 18, 91-93.
https://doi.org/10.1016/j.tjem.2018.08.001
[25]  Mas’ud, A.A., Ardila-Rey, J.A., Albarracín, R., Muhammad-Sukki, F. and Bani, N.A. (2017) Comparison of the Performance of Artificial Neural Networks and Fuzzy Logic for Recognizing Different Partial Discharge Sources. Energies, 10, Article No. 1060.
https://doi.org/10.3390/en10071060

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