Fuzzy rule based approach for predicting weld bead geometry in gas tungsten arc welding

被引：10

作者：

Ghanty, P. ^{[1
]}

Paul, S. ^{[2
]}

Roy, A. ^{[3
]}

Mukherjee, D. P. ^{[1
]}

Pal, N. R. ^{[1
]}

Vasudevan, M. ^{[4
]}

Kumar, H. ^{[4
]}

Bhaduri, A. K. ^{[4
]}

机构：

[1] Indian Stat Inst, Elect & Commun Sci Unit, Kolkata 700108, W Bengal, India

[2] Simplex Infrastruct Pvt Ltd, IT Dept, Kolkata 700087, W Bengal, India

[3] Indian Stat Inst, Comp Vis & Pattern Recognit Unit, Kolkata 700108, W Bengal, India

[4] Indira Gandhi Ctr Atom Res, Mat Joining Sect, Mat Technol Div, Kalpakkam 603102, Tamil Nadu, India

来源：

SCIENCE AND TECHNOLOGY OF WELDING AND JOINING | 2008年 / 13卷 / 02期

关键词：

arc welding; gas tungsten arc welding; fuzzy rule; exploratory data analysis; artificial neural networks; multilayer perceptron; radial basis function;

D O I：

10.1179/174329308X271751

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The use of fuzzy rule based systems to model the relationship between weld control parameters and the weld bead geometry features is explored in this paper. The Takagi-Sugeno model with linear functions of the inputs is used as the rule consequents. Given some training data, the authors use exploratory data analysis to find an initial rule base. The system parameters, e. g. consequent parameters, are estimated using a mixture of least square error (LSE) method and gradient search. The system is tested on three datasets and the performance is found to be satisfactory compared to the multilayer perceptron (MLP) and radial basis function (RBF) neural networks based systems.

引用

页码：167 / 175

页数：9

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