关键词: 空洞锻合, 粒子群优化, 支持向量机, 临界压下率

Abstract: The prediction of reduction ratio for pore closure in heavy forgings stock is limited to the finite element numerical simulation and some complex mathematical formula.This paper proposes a new forecasting method，using the SVM model to predict the critical reduction ratio of pore closure.Several major factors are selected that affecting pore closure as the input features of SVM.SVM’s nuclear parameters with PSO are optimized in order to enhance its accuracy，LIB-SVM toolbox is combined to train a SVM model.The model can predict the critical reduction ratio of pore closure in heavy forging quickly，compared its predictions with computer simulation results，the correlation coefficient reaches almost 85%，it has a good prediction performance.

Key words: pore closure, Particle Swarm Optimization（PSO）, Support Vector Machine（SVM）, critical reduction ratio