Optimization of Drilling and Countersink Process Parameters Based on Improved BP Neural Network Algorithm
Aiming at the current problem that the selection of drilling and countersink process parameters of industrial robots mainly depends on empirical method,a method of optimizing the technological parameters of drilling and countersink based on improved BP neural network algorithm is proposed.The technological process of drilling and countersink is ana-lyzed,and orthogonal experiment design and correlation analysis are carried out for the relationship between technological parameters and processing quality.Aiming at the deficiency of the harris eagle algorithm,two aspects of prey escape proba-bility and prey jump intensity are improved,the improved Harris Hawk algorithm is used to optimize the BP neural network,and the mathematical model of technological parameters optimization is established based on the improved BP neural net-work algorithm.The fmincon function is used to solve the optimal technological parameters and experimental verification is carried.The results show that the aperture accuracy and countersink depth accuracy of the optimized process parameters are 17.9％and 26.5％,which are higher than those determined by the empirical method respectively.It not only meets the re-quirements of machining quality,but also ensures the machining efficiency.
万方数据
维普
