Casting Performance Optimization of High Strength Aluminum Alloy for Automobile Based on Neural Network Algorithm

In order to optimize the casting performance of high-strength aluminum alloy for automobile, taking the alloy element, element content, melting temperature and pouring temperature as the input layer parameters, the fluidity of the alloy as the output layer parameter, and selecting the Purelin function, Tansig function and Trainlm function as the output layer transfer function, implicit layer transfer function and training function,the 4×16×4×1 four-layer neural network model was constructed by using the neural network algorithm. Learning training, prediction analysis, and validation of untrained samples of neural network models were performed. The results show that the neural network model converges after 8892 iterations,and the relative training error of the model is 3.50%-6.41%, and the average relative training error is 4.76%. The relative prediction error is 4.25%-5.56%, and the average relative prediction error is 4.88%. The neural network model has strong prediction ability and high prediction accuracy, which can be used to optimize and predict the casting properties of high strength aluminum alloy for automobile.