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

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.

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[57]  Sehun Rhee,et al.  Estimation of weld bead size in CO2 laser welding by using multiple regression and neural network , 1999 .

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[59]  Abdul-Ghani Olabi,et al.  Application of Response Surface Methodology in Describing the Residual Stress Distribution in CO2 Laser Welding of AISI304 , 2007 .

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[69]  Liu Li.  Predicting effects of diffusion welding parameters on welded joint properties by artificial neural network , 2001 .

[70]  Thomas Sourmail,et al.  Mechanical Property Prediction of Commercially Pure Titanium Welds with Artificial Neural Network , 2009 .

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