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Artificial neural networks - An aid to welding induced ship plate distortion?

Lookup NU author(s): Dr Martyn Lightfoot, Professor George Bruce

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Abstract

A preliminary study on the potential application of artificial neural networks In welded structures was expanded to metal inert gas welding of steel plates of grades D and DH 36. The main controllable variables were plate thickness, steel grade, plate cutting process, and heat input. A series of welded plates of each grade was manufactured, covering plate thicknesses of 6 and 8 mm. The topography of each welded plate was evaluated after tacking the plates together and after welding, allowing the actual distortion to be calculated. It was established that a multilayer perceptron network architecture configuration accurately represented the distortion for the 6 mm thickness plate, and for the 8 mm thickness plate after treatment of the data. The data generated were used to develop the PREDICTOR software package, which allows a distortion prediction to be produced, and to carry out a sensitivity analysis. Heat input was found to be the most sensitive factor related to distortion, with carbon content of the plates, yield/tensile strength ratio, carbon equivalent, and steel grade also having significant effects. Some test plates were modelled using finite element method software packages: the initially poor agreement was improved via the addition of significant detail, but the finite element model by its nature will normally predict symmetrical distortion from a symmetric weld, whereas the artificial neural network model developed was capable of predicting the asymmetric distortion observed in reality. © 2005 Institute of Materials, Minerals and Mining.

Publication metadata

Author(s): Lightfoot MP, Bruce GJ, McPherson NA, Woods K

Publication type: Article

Publication status: Published

Journal: Science and Technology of Welding and Joining

Year: 2005

Volume: 10

Issue: 2

Pages: 187-189

Print publication date: 01/04/2005

ISSN (print): 1362-1718

ISSN (electronic): 1743-2936

Publisher: Maney

URL: http://dx.doi.org/10.1179/174329305X36089

DOI: 10.1179/174329305X36089

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