Abstract
Angular distortions are one of the most common types of distortions frequently observed in thin butt-welded
plates. Generally, welding-induced angular distortions lead to additional costs for rework. Therefore,
estimation of angular distortions prior to welding can be beneficial from several standpoints. In this
study, welding-induced angular distortions in single-pass butt-welded 304 stainless steel plates were
predicted using artificial neural networks. In order to predict angular distortions a multilayer feed forward
back propagation neural network was created using MATLAB. The input data of the neural network
were obtained from a series of finite element simulations for a wide range of plate dimensions. The three dimensional
finite element simulations have been performed using uncoupled thermo-elasto-plastic
analysis. In order to validate the numerical results a series of experiments have been performed and temperature
histories and angular distortions have been measured for two different cases. The results of this
study revealed that the neural network created in this study can accurately predict welding angular distortions
produced in single-pass butt welded plates. ©2012 Elsevier B.V. All rights reserved.
Original language | English |
---|---|
Journal | Computational Materials Science |
Volume | 62 |
Pages (from-to) | 152–159 |
ISSN | 0927-0256 |
DOIs | |
Publication status | Published - 2012 |
Externally published | Yes |
Keywords
- Butt-weld
- Angular distortion
- Thin plate
- Finite element method
- Artificial neural network