Prediction of multiwire submerged arc weld bead shape using neural network modelling

Abstract

A significant problem faced by today's welding engineers is the need to relate welding parameters to the quality of the finished weld. This is usually achieved by experience, and necessitates many experimental trials, eventually leading to optimal welding parameters. Important characteristics in the evaluation of linepipe seam weld quality are the weld bead shape and size, which can have a significant effect on the microstructure and mechanical properties of the weldment through heat flow effects. The present paper describes the application of neural network techniques to the prediction of the outer diameter weld bead shape for three wire, single pass per side, submerged arc, linepipe seam welds, using the weld process parameters as inputs. Novel methods of digitisation of weld macrostructures produced under different experimental conditions, used in the training of the neural network, are discussed. The contribution of a particular welding process parameter (input relevance) to the variation in the final weld bead shape is also considered. It is shown that it is possible to develop a neural network model that will predict the shape of an entire weld bead, without the necessity to assume it to be symmetric, with a relatively high degree of confidence.