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

被引:9
作者
Lightfoot, MP
Bruce, GJ
McPherson, NA
Woods, K
机构
[1] BAE Syst, Naval Ships, Glasgow GS1 4XP, Lanark, Scotland
[2] Univ Newcastle Upon Tyne, Dept Marine Technol, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
[3] S Teesside Works, Corus Construct & Ind, Redcar TS10 5QW, England
关键词
artificial neural networks; metal inert gas welding; steel plates; plate thickness; steel grade; plate cutting process; heat input; weld distortion; multilayer perceptron network; software; sensitivity analysis; finite element method;
D O I
10.1179/174329305X36089
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
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页码:187 / 189
页数:3
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