Neural network in predicting the mechanical strength of Al6061/SiC composites used in aerospace industries
Subject Areas : Computer Engineering
1 - دانشکده فنی و مهندسی، واحد دامغان، دانشگاه آزاد اسلامی، دامغان، ایران
Keywords: Fiber Type, Heat Treatment, Yield Strength, Ultimate Tensile Strength, Elastic Modulus ,
Abstract :
In this research, the effect of three parameters, the percentage and type of SiC reinforcing material and the type of thermal cycle of composite manufacturing on the elastic modulus, yield strength and ultimate strength of Al/SiC composite have been investigated. For this purpose, the information related to composite with Al6061alloy reinforced with SiC particles was extracted. After normalizing the input and output information, the perceptron recurrent network was designed and it was tried to extract the optimal parameters by changing different parameters of the network such as the frequency of network training, changing the learning coefficient of the network and weight and bias coefficients and comparing the sum of squared errors in different conditions. Finally, by defining the adaptive learning coefficient in the network, it was tried to improve the speed and accuracy of the network. The results showed that by repeating the network training 10000000 times, the sum of squares of error was reduced to the range of 7-10. Also, the lowest error sum of squares is related to the learning coefficient α=0.002. The results related to the definition of the adaptive learning coefficient showed that if the network is trained 100,000 times, the training speed decreases to 60,000 times with the same error for the adaptive learning coefficient and the constant learning coefficient. In other words, if the adaptive learning coefficient is used, the training speed and error reduction will be higher than using the constant learning coefficient.
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