Artificial Neural Networks for detecting instability trends in different workpiece thicknesses in a machining process

This paper presents the use of artificial neural networks to diagnose degraded behaviours in wire electrical discharge machining (WEDM). The detection in advance of the degradation of the cutting process is crucial since this can lead to the breakage of the cutting tool (the wire), reducing the process productivity and the required accuracy. Concerning this, previous investigations have identified different types of degraded behaviors in two commonly used workpiece thicknesses (50 and 100 mm). This goal was achieved by monitoring different functions of the characteristic variables of the discharges. However, the thresholds achieved by these functions depended on the workpiece thickness. Consequently, the main objective of this work is to detect the process degradation in different workpiece thicknesses using one unique empirical model. Since neural network techniques are appropriate for stochastic and nonlinear nature processes, its use is investigated here to cope with different workpiece thicknesses. The results of this work show a satisfactory performance of the presented approach.