Gear Transmission Optimization Design based on Intelligent Algorithm

The gear designed using traditional method has large size, and probably is not the most optimal design scheme, and the design cycle is long leading to low efficiency. The relationship between the tooth shape coefficient and the number of teeth can be mapped into radial basis function neural network. Weights of hidden layer and output layer have been optimized using genetic algorithm. Finally, genetic algorithm is used in reasonable selection of crossover probability and mutation probability for gear optimization design. This algorithm is used to design two grade helical cylindrical gear reducer. The result shows that the algorithm performance is better than the original genetic algorithm and traditional methods. This algorithm has wide application prospect in mechanical optimization design due to its high efficiency.