Abstract:
This study aimed to predict and optimize the surface roughness for
work piece type (St 42crmo4) in straight turning process, the study will
focus on three cutting parameters that effect on the surface roughness
which is the cutting speed feed rate and depth of cut while maintaining
the other parameter constant, to predict and optimize the response two
models are developed, the first is mathematical second order model by
using response surface methodology to analyze the cutting parameter
effects on surface roughness , and the second is artificial Neural Networks
model to predict and optimize the response, the experiments were
conducted by three level full factorial design methodology in (CNC) lathe
machine type (TB-15Z ~ NL635SCZ), the response variable namely the
surface roughness was measured using Portable surface roughness tester
(Surf-test SJ-210 SERIES), the effect of process parameters with the
output variable were predicted which indicates that the cutting speed has
significant role in producing least surface roughness followed by feed and
at least depth of cut, and the optimized parameters which give the optimal
response value are gained by utilizing (ANN) model.
