Essam B. Moustafa

TL;DR: In this article , the authors proposed a fine-tuned artificial intelligent model to predict the thermal efficiency and water yield of the solar still, which consists of a traditional artificial neural network model optimized by a meta-heuristic optimizer called humpback whale optimizer.

TL;DR: The results revealed that multi-pass FSP causes a homogeneous distribution and good dispersion of Al2O3 in the metal matrix, and consequently an increase in the hardness of the matrix composites.

TL;DR: In this paper, the wear resistance and microhardness behavior of hybrid nanocomposites with different volume fractions of SiC and BN nano-powders were investigated experimentally.

TL;DR: In this paper, two hybrid artificial neural network (ANN) models are used to predict the process responses after training them using the experimental results, and the prediction accuracy of the two models are enhanced via integration with two different metaheuristic optimization algorithms, namely particle swarm optimization (PSO) and flower pollination algorithm (FPA).

TL;DR: In this article, a new artificial intelligence-based predictive model for friction stir welding of dissimilar polymeric materials is introduced, which is used to correlate the joint characteristics (tensile strength, joint efficiency and extensibility) with the welding variables (rotational tool speed, welding speed and tool tilt angle).