TITLE:
Artificial Neural Network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium
AUTHORS:
Anukwonke Maxwell Chukwuma, Chibueze Ikechukwu Godwills, Cynthia C. Nwaeju, Osakwe Francis Onyemachi
KEYWORDS:
Al-5%Mg Alloy, Neodymium, Artificial Neural Network, Fuzzy Logic, Average Grain Size and Mechanical Properties
JOURNAL NAME:
International Journal of Nonferrous Metallurgy,
Vol.11 No.1,
January
17,
2024
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).