Prediction of Mechanical Properties of Welded Joints Based on Support Vector Regression

被引：5
|
作者：
Gao Shuangsheng [1 ]
Tang Xingwei [1 ]
Ji Shude [1 ]
Yang Zhitao
机构：
[1] Shenyang Airspace Univ, Shenyang 110136, Peoples R China
来源：
2012 INTERNATIONAL WORKSHOP ON INFORMATION AND ELECTRONICS ENGINEERING | 2012年 / 29卷
关键词：
Support vector regression; mechanical properties; modeling;
D O I：
10.1016/j.proeng.2012.01.157
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
Support vector regression (SVR) networks were developed based on kernel functions of linear kernel, polynomial kernel, radial basis function (RBF) and Sigmoid in this paper. The input parameters of TC4 alloy plates include weld current, weld speed and argon flow while the output parameters include tensile strength, flexural strength and elongation. The SVR networks were used to build the mechanical properties model of welded joints and make predictions. A comparison was made between the predictions based on SVR and that based on adaptive-network based fuzzy inference system (ANFIS). The results indicated that the predicted precision based on SVR with radial basis kernel function was higher than that with the other three kernel functions and that based on ANFIS. (C) 2011 Published by Elsevier Ltd.
引用
收藏
页码：1471 / 1475
页数：5
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