Abstract

This article reports an investigation of the influence of process parameters on the obtainable dimensional accuracy when drilling glass using abrasive jet machining. In particular, holes were drilled out of glass sheets, and the effects of standoff distance, nozzle diameter, particle grain size and applied pressure on the kerf taper were examined. An artificial neural network technique was used to establish a precise model of kerf taper as a function of the process parameters. The proposed model was then optimised, and the conditions to minimise the kerf taper were identified using a genetic algorithm. The results revealed that standoff distance has a major effect on kerf taper, and it proved possible to substantially reduce the kerf taper by applying an axial feed to the nozzle so that the standoff distance is kept constant during the machining process.

Highlights

  • The results showed that artificial neural network (ANN) models could be used to model the process outcomes as a function of the input process parameters and successfully predict the performance of the process

  • It is observed that abrasive jet machining (AJM) with soda lime glass, Standoff distance (SoD) has a major effect on kerf taper of the holes produced

  • It is worth emphasising that the validation experiment was carried out for conditions set as close as possible to optimum values to test the genetic algorithm (GA) and ANN models

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Summary

Introduction

The quality of surfaces generated by AJM has been discussed by many investigators seeking to estimate the effects of process parameters. Balasubramaniam et al.[12,13,14] found the surface generated had a reverse bell-mouthed shape, with entry side diameter in the target material depending on the values of the process parameters.

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