Application of support vector machine in the prediction of mechanical property of steel materials

作     者：Ling Wang Zhichun Mu Hui Guo 

作者机构：Information Engineering School University of Science and Technology Beijing Beijing 100083 China 

出 版 物：《Journal of University of Science and Technology Beijing》 (北京科技大学学报（英文版）)

年 卷 期：2006年第13卷第6期

页      面：512-515页

核心收录：

学科分类：0806[工学-冶金工程] 08[工学] 0818[工学-地质资源与地质工程] 080203[工学-机械设计及理论] 0815[工学-水利工程] 0805[工学-材料科学与工程（可授工学、理学学位）] 0813[工学-建筑学] 0703[理学-化学] 0802[工学-机械工程] 0814[工学-土木工程] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 

主　　题：mechanical properties support vector machine support vector regression chemical composition hot-rolling parameters 

摘      要：The investigation of the influences of important parameters including steel chemical composition and hot rolling parameters on the mechanical properties of steel is a key for the systems that are used to predict mechanical properties. To improve the prediction accuracy, support vector machine was used to predict the mechanical properties of hot-rolled plain carbon steel Q235B. Support vector machine is a novel machine learning method, which is a powerful tool used to solve the problem characterized by small sample, nonlinearity, and high dimension with a good generalization performance. On the basis of the data collected from the supervisor of hotrolling process, the support vector regression algorithm was used to build prediction models, and the off-line simulation indicates that predicted and measured results are in good agreement.