MODELLING AND OPTIMIZING OF A MIG WELDING PROCESS—A CASE STUDY USING EXPERIMENTAL DESIGNS AND NEURAL NETWORKS

Abstract

This paper describes an application of an integrated method using experimental designs and neural network technologies for modelling and optimizing a metal inert gas (MIG) welding process. To achieve optimization, the process parameters must be adjusted in such a way that the deviations from target are minimized while the robustness to noise and to process fluctuations are maximized. This new method consists of an experiment reference template for designing and collecting training data samples, and a parallel distributed computational adaptive neural network system to provide a powerful tool for data modelling and empirical investigations. The relevant data is established using experimental design methods and highlighted in the case study. An adaptive GaRBF neural network is used to approximate the stochastically non-linear dynamics of the welding process to optimize the basic welding parameters. The neural network is trained with welding experimental data, tested and compared in an actual welding environment in terms of its ability to determine weld quality. The results show that the proposed adaptive neural network is capable of mapping the complex relationships between the welding parameters and the corresponding output weld quality. The implementation for this case study was carried out using a ‘semi-automatic’ welding facility, to mass weld a 20″ × 0.438″ pin/box onto a 20″ × 0.5″ × 37′ pipe (tubular drilling products), in an actual workshop which makes oilfield equipment. The entire range of welding combinations that might be experienced during actual welding operations is included to study the weld quality. © 1997 by John Wiley & Sons, Ltd.