Genetically optimized neural network systems (GONNS) was developed to simulate the intelligent decision-making capability of human beings. After they are trained with experimental data or observations, GONNS use one or more artificial neural networks (ANN) to represent complex systems. The optimization is performed by one or more genetic algorithms (GA). In this study, the GONNS was used to estimate the optimal operating condition of the friction stir welding (FSW) process. Five separate ANNs represented the relationship between two identical input parameters and each one of the considered characteristics of the welding zone. GA searched for the optimized parameters to make one of the parameters maximum or minimum, while the other four are kept within the desired range. The GONNS was found as an excellent optimization tool for FSW.
In this study mechanical and thermal properties of epoxy resin reinforced with different numbers of nanofiber layers which produced with electrospinning method was investigated. Solution of 10 wt% of polyacrylonitrile (PAN) in N,N-dimethylformamide (DMF) was used for electrospinning. The diameters of the obtained nanofibers were in the range of 380-420 nm. The average thickness of the produced nanofiber layer was about 200 µm. The special molds were prepared to produce the laminated composite plates. The tensile tests show that the using of nanofiber PAN layers increase the tensile force 34.54% and decrease the elongation 8.87% in comparison with neat epoxy. The fracture surfaces of the specimens were inspected by using optical and scanning electron microscopy (SEM). The thermal properties of the nanofiber layered composites were determined by thermo gravimetric analysis (TGA) and differential scanning calorimetry (DSC) analysis. It was observed that the glass transition temperature increased parallel to this as the number of PAN layers increased and rose up to 86ᵒC, while the thermal stability did not show much effect of PAN layers.
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