Parametric Design of Die Surface Based on BP Neural Network Algorithm

In the composition of automobile parts, the proportion of automobile body parts exceeds that of chassis and engine in terms of parts weight and manufacturing cost. Therefore, improving the ability of independent research and development and manufacturing of automobile body parts is the core content of enhancing independent research and growth of automobile industry. In order to meet this requirement, mold manufacturing industry makes full use of new technologies such as NC machining and mold computer-aided manufacturing, which makes mold processing technology enter a new stage dominated by NC machining and mold computer-aided manufacturing. Based on the principle of error distribution and surface evaluation, combined with back propagation neural network (BPNN), this paper analyzes the precision control and error distribution of each link in the stage of parametric design of die surface, discusses the quantitative analysis method of precision error in the stage of reverse parametric design of die surface, and realizes the precision control of reverse parametric design of die surface. The simulation results show that the accuracy of this algorithm is 26.88% higher than that of traditional SVM. Reasonable distribution and control of precision can effectively meet the precision requirements of parametric design of die surface based on BPNN.