Abstract:
The prediction of machining process capabilities
is important in process parameter optimization and
improvement of machining performance characteristics. This
paper, presents the prediction of Wire-EDM input parameters
for surface roughness using Artificial Neural Network and
Response Surface Methodology. Ti-6Al-4V is an alpha-beta
alloy widely applied in industry due of its excellent combination
of mechanical properties. However, this alloy is found to be
difficult to machine by means of conventional machining
processes because of its high melting temperature, high chemical
reactivity, and low thermal conductivity. Nevertheless, nonconventional machining processes such Wire-EDM are able to
overcome the challenge in machining Ti-6Al-4V. Response
Surface Methodology (RSM) based on Central Composite
design is used to evaluate and optimize the effect of pulse on time
(Ton), discharge current (I) and open circuit voltage (UHP) on
surface roughness (SR). Analysis of Variance revealed that
open-circuit voltage is the most significant parameter affecting
the obtained surface roughness followed by the discharge
current. Parametric variation shows that lower surface
roughness can be obtained at lower levels of UHP and I. The
main contribution of this paper is the prediction of wire-EDM
machining process parameters for a given surface roughness
using Artificial Neural Network (ANN). The developed ANN
model revealed to be 97.155% accurate with an average
prediction error of 2.845%. The predictive capability of the
developed ANN model is found to be satisfactory and the model
can be successfully used for predicting machining process
parameters for desired surface roughness in wire electrical
discharge machining process.
