Optimization of the Top Guard for Excavator Based on Neural Genetic Algorithm

The main function of top guard for excavator is to safeguard the lives and safety of the drivers when the vehicles encounter falling-object, it should have the lowest mass as long as it meets the performance standard. In order to improve the protection ability of protection structure for drivers and reduce manufacturing cost and design cycles, the optimization mathematical model is established, where the mass is defined as objective function and the performance is taken as constraints condition. Because of the material non-linearity, geometry non-linearity and contact non-linearity between the design variables and performance, explicit expression is hard to establish. And all the design programs require a large amount of calculation for finite element analysis owing to non-linear, large deformation. In order to solve this problem, the optimization method based on neural network and genetic algorithm is put forward, which calculates the response of protection structure through selecting sample points, trains neural network to simulate the relations between design variables and performance, and utilizes the genetic algorithm to solve the global optimal point. Taking the top guard of excavator as an example for optimization design, the paper develops computation program and optimization program for top guard is also determined.