Artificial Neural Network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium — Oak Academic Publishing
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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
Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria
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Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria
,
Department of Mechanical Engineering, Nigeria Martine University, Okerenkoko, Nigeria
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Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria
1 Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria
2 Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria
3 Department of Mechanical Engineering, Nigeria Martine University, Okerenkoko, Nigeria
4 Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria
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).
KeywordsAl-5%Mg AlloyNeodymiumArtificial Neural NetworkFuzzy LogicAverage Grain Size and Mechanical Properties
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