Using a neural network for predicting the average grain size in friction stir welding processes

被引：84

作者：

Fratini, Livan ^{[1
]}

Buffa, Gianluca ^{[1
]}

Palmeri, Dina ^{[1
]}

机构：

[1] Univ Palermo, Dipartimento Tecnol Meccan Prod & Ingn Gest, I-90128 Palermo, Italy

来源：

COMPUTERS & STRUCTURES | 2009年 / 87卷 / 17-18期

关键词：

Friction stir welding; Aluminum alloys; Continuous dynamic recrystallization; Neural networks; FEM; MECHANICAL-PROPERTIES; FINITE-ELEMENT; AL; MICROSTRUCTURE; IMPROVEMENT; JOINTS; MODEL;

D O I：

10.1016/j.compstruc.2009.04.008

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In the paper the microstructural phenomena in terms of average grain size occurring in friction stir welding (FSW) processes are focused. A neural network was linked to a finite element model (FEM) of the process to predict the average grain size values. The utilized net was trained starting from experimental data and numerical results of butt joints and then tested on further butt, lap and T-joints. The obtained results show the capability of the Al technique in conjunction with the FE tool to predict the final microstructure in the FSW joints. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：1166 / 1174

页数：9

共 31 条

[1] Microstructural and mechanical properties of friction stir welded aluminum/copper lap joints [J].