Prediction of the Average Grain Size in Submerged Friction Stir Welds of AA 6061-T6

被引：8

作者：

Rathinasuriyan, C. ^{[1
]}

Sankar, R. ^{[1
]}

Shanbhag, Avin Ganapathi ^{[1
]}

SenthilKumar, V. S. ^{[1
]}

机构：

[1] Anna Univ, CEGC, Dept Mech Engn, Chennai 600025, Tamil Nadu, India

来源：

MATERIALS TODAY-PROCEEDINGS | 2019年 / 16卷

关键词：

Submerged Friction Stir Welding (SFSW); Response Surface Methodology (RSM) and Artificial Neural Network (ANN); MECHANICAL-PROPERTIES; ALUMINUM-ALLOY; OPTIMIZATION; PARAMETERS; STRENGTH; SPEED;

D O I：

10.1016/j.matpr.2019.05.176

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this study, submerged friction stir welded AA6061-T6 alloy was investigated by experiments, RSM and ANN in order to determine the average grain size in the nugget zone. RSM based on the Box-Behnken design with three parameters, three levels, and 15 runs, was used for the planning, conduct, execution and development of the models. Two models, linear (second-order polynomial equation) and nonlinear (neural network) were developed for predicting the average grain size (mu m) of SFS welded AA6061-T6 alloy as the function of rotational speed (rpm), welding speed (mm/min) and water head (mm) by using Minitab and Matlab software. The effects of those factors have also been studied. The performances of the RSM and ANN model compared using the test data. The ANN model yields more accurate results compared to the RSM model. (C) 2019 Elsevier Ltd. All rights reserved.

引用

页码：907 / 917

页数：11

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