Optimisation of weld deposition efficiency in pulsed MIG welding using hybrid neuro-based techniques
The weld quality depends primarily on the degree of arc stability and the bead characteristics in gas metal arc welding. The weld deposition has to be enhanced to make the process economically feasible. This article addresses modelling and optimisation of deposition efficiency in highly non-linear pulsed metal inert gas welding. The design of experiments was performed using central composite response surface methodology for the model development. The back propagation neural network technique was found to be better than the response surface regression model. Two global optimisation techniques, namely, genetic algorithm and differential evolution, were then applied to maximise the deposition efficiency. The capability to identify the hidden optimum solutions using differential evolution technique was found to be better than genetic algorithm.
optimisation arc stability peak voltage pulse frequency pulse on-time torch angle neuro-ga neuro-de