Prediction model of needle valve body extrusion grinding process based on PSO-SVR

Abstract

The needle valve body is one of the core components of the diesel engine injection system, there is a complicated non-linear relationship between process parameters and process effects in its extrusion and grinding process, and it is difficult to establish specific mathematical expressions to describe the process rules. Therefore, this paper adopts support vector regression (SVR) establish a needle valve body extrusion grinding process model, and uses flow coefficient and grinding efficiency as dual output prediction targets. The particle swarm optimization algorithm (PSO) with better global search ability is introduced to optimize and improve the model. The results show that the optimized model has a smaller error between the predicted value and the actual value of the data, and the prediction accuracy is significantly improved. The model can reflect the process law of the needle valve body extrusion and grinding process to a certain extent, and it can provide certain guidance for selecting process parameters in the extrusion and grinding process.