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

Abstract

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

Industrial‐scale vacuum casting with silicone molds: A review

“…The exact time pressure curve during casting can be controlled manually and depends on the material and the geometry of the mold. A variant of the conventional VC, which is becoming increasingly popular, is the so‐called vacuum differential pressure casting (VDPC; Figure 1k) [24,25]. A differential pressure system provides an upper chamber containing the casting and mixing unit, which is separated from the lower mold chamber by a partition wall where the funnel passes through.…”