Abrasive jet machining of glass: Experimental investigation with artificial neural network modelling and genetic algorithm optimisation

Abstract

The paper presents an experimental-based study of abrasive jet machining (AJM) considering the effect of changing process parameters. A series of drilling tests were carried out on glass workpieces using sand as the abrasive powder. The influence of each process parameter; applied air pressure, standoff distance, nozzle diameter, particle grain size and impact angle on the machining performance was determined in terms of the resultant material removal rate (MRR). The experimental results revealed that MRR was highly dependent on the kinetic energy of the abrasive particles, with the applied pressure the dominant parameter. The experimental results were compared with an erosion rate model previously published by Jafar et al. Though correct trends were predicted, there was a large discrepancy between model and measured values. An artificial neural network (ANN) was utilised to model the MRR more precisely, particularly to establish relationships between applied machining parameters and experimentally measured MRR and achieved a maximum error of only 5.3%. A Genetic Algorithm (GA) was applied to optimise the model and identify the conditions to maximise the MRR. The results were experimentally validated and good agreement found between the experimental results obtained and the ANN and GA predictions.