Process Parameter Prediction of Differential Pressure Vacuum Casting Based on Support Vector Machine

To reduce warpage deformation of the differential pressure vacuum casting (DPVC) products and to improve product quality, One prediction method for process parameters based on support vector machine (SVM) and artificial fish-swarm algorithm (AFSA) is proposed.Firstly sample test data is abtained by using orthogonal experimental design and numerical simulation to construct models to forecast warpage of DPVC product based on SVM. Simultaneously to improve the predictive accuracy of the model, AFSA is introduced to optimize the SVM model. And then using this model recommends and adjusts the DPVC process in order to achieve quality control. Finally , through the analysis of a mouse shell , the validity of the method proposed is verified, providing a feasible method for DPVC product quality control