论文信息 - Process Parameter Prediction of Differential Pressure Vacuum Casting Based on Support Vector Machine

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

Xu Zhang | Qingxi Hu | Hai Guang Zhang | Zhuang Ya Zhang

[1] Ya-Jung Lee,et al. 2006A-G4-6 Mold-flow Analysis for Manufacturing FRP Structures in Vacuum Assisted Resin Transfer Molding , 2006 .

[2] Yuehua Gao,et al. Surrogate-based process optimization for reducing warpage in injection molding , 2009 .

[3] Xin Guan,et al. An Improved Artificial Fish Swarm Algorithm and its Application , 2012 .

[4] S. Theodoridis,et al. Reduced Convex Hulls: A Geometric Approach to Support Vector Machines [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[5] Xiang Guo-qi. Genetic Optimization Design Based on Support Vector Regression Metamodeling , 2009 .

[6] Bernhard Schölkopf,et al. Feature selection for support vector machines by means of genetic algorithm , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.

[7] Hasan Kurtaran,et al. Warpage optimization of a bus ceiling lamp base using neural network model and genetic algorithm , 2005 .

[8] Jian-Wei Ma,et al. An improved artificial fish-swarm algorithm and its application in feed-forward neural networks , 2005, 2005 International Conference on Machine Learning and Cybernetics.