This work introduces an approach for optimization machinability measures of power
consumption, machining time, and the surface roughness (PMS). This approach is
starting with market customer’s demands, passing by optimizing the machinability
measures (PMS), and ending by the optimized cutting conditions. The fuzzy logic was
used to defne the weights of each of required machinability measurement using
method through expert rules depending on factory requirements. Genetic algorithm
was formulated for giving optimum output values based on the customer’s demands.
A neural network was designed for controlling the input cutting conditions with the
PMS output parameters. The proposed soft computing technique creates reasonable
results compared to experimental results and gives rich investigations for optimizing
the output parameters not only for increasing productivity and quality demands but
also for saving power consumed. The variation of consumed power, machining time,
and surface roughness was calculated based on diferent customer demand levels.
When the machining time and power consumed importance increased, the proposed
technique reduced them by about 20% and 10% for the testes case.
