Article citationsMore>>

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

has been cited by the following article:

• 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).