Optimization of different welding processes using statistical and numerical approaches - A reference guide

被引:302
作者
Benyounis, K. Y. [1 ]
Olabi, A. G. [2 ]
机构
[1] Garyounis Univ, Dept Ind Engn, Benghazi, Libya
[2] Dublin City Univ, Sch Mech & Mfg Engn, Dublin 9, Ireland
关键词
quality of weld; welding; RSM; ann; Taguchi; optiinization;
D O I
10.1016/j.advengsoft.2007.03.012
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Welding input parameters play a very significant role in determining the quality of a weld joint. The joint quality can be defined in terms of properties such as weld-bead geometry, mechanical properties, and distortion. Generally, all welding processes are used with the aim of obtaining a welded joint with the desired weld-bead parameters, excellent mechanical properties with minimum distortion. Nowadays, application of design of experiment (DoE), evolutionary algorithms and computational network are widely used to develop a mathematical relationship between the welding process input parameters and the output variables of the weld joint in order to determine the welding input parameters that lead to the desired weld quality. A comprehensive literature review of the application of these methods in the area of welding has been introduced herein. This review was classified according to the output features of the weld, i.e. bead geometry and mechanical properties of the welds. (c) 2007 Elsevier Ltd. All rights reserved.
引用
收藏
页码:483 / 496
页数:14
相关论文
共 77 条
[61]  
Radaj D., 1992, Heat effects of welding
[62]   Analysis of the explosive cladding of cu-low carbon steel plates [J].
Raghukandan, K .
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2003, 139 (1-3) :573-577
[63]  
Raveendra J., 1987, J. Met. Constr, V19, p31R
[64]   Prediction of multiwire submerged arc weld bead shape using neural network modelling [J].
Ridings, GE ;
Thomson, RC ;
Thewlis, G .
SCIENCE AND TECHNOLOGY OF WELDING AND JOINING, 2002, 7 (05) :265-279
[65]  
SAMPATH K, 2005, WELD J AWS AUG
[66]  
Seshank K, 2003, IKE'03: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE ENGINEERING, VOLS 1 AND 2, P149
[67]   STATISTICAL MODELING OF NARROW-GAP GTA WELDING WITH MAGNETIC ARC OSCILLATION [J].
STARLING, CMD ;
MARQUES, PV ;
MODENESI, PJ .
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 1995, 51 (1-4) :37-49
[68]   Artificial neural networks for modelling the mechanical properties of steels in various applications [J].
Sterjovski, Z ;
Nolan, D ;
Carpenter, KR ;
Dunne, DP ;
Norrish, J .
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2005, 170 (03) :536-544
[69]  
Sterjovski Z, 2004, P 4 INT C PIP TECHN, P1233
[70]   Modeling, optimization and classification of weld quality in tungsten inert gas welding [J].
Tarng, YS ;
Tsai, HL ;
Yeh, SS .
INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 1999, 39 (09) :1427-1438