Computationally intelligent modelling of the plasma cutting process

This paper investigates the applicability of two computational intelligence methods for the plasma cutting process modelling: artificial neural networks and adaptive neuro-fuzzy inference systems. After exploring the possibilities of neural networks learning from the experimental data for the prediction of the plasma cutting parameter, the adaptive neuro-fuzzy inference system approach was used in order to compare the prediction properties of neural networks and adaptive neuro-fuzzy models. These two methods resulted in the kerf width prediction models for different combinations of input process parameters: cutting current, cutting speed and material thickness, whose significance was assessed by multi-criteria analysis. Descriptive statistics and a visual exploration of two sets of neural-network and adaptive neuro-fuzzy modelled data showed good agreement with the experimental results and their trends. This was additionally confirmed by the t-test and Analysis of Variance, which also provided the selection of the most favourable method for plasma cutting modelling. Accordingly, the adaptive neuro-fuzzy inference systems showed superior modelling capabilities over artificial neural networks for this particular problem setting.