International Journal of Advance Research and Innovation

 
1. Introduction 
The operating parameters that contribute to turning 
process are cutting speed, Feed rate, Depth of cut. 
Vibrations, tool wear, tool life, surface finish and cutting 
forces etc are also in direct relation with values selected for 
process parameters. Hence to improve the efficiency of 
process and quality of the product it is necessary to control 
the process parameters. The product quality depends very 
much on surface roughness. Increase of surface roughness 
also leads to decrease of product quality. In field of 
manufacturing, the surface finish quality is important and 
influences the functioning of a component. Surface 
roughness has been receiving attention for many years in the 
industries. It is an important design feature, such as parts 
subject to fatigue loads, precision fits, and fastener holes 
and so on. In terms of tolerances, surface roughness is one 
of the most crucial constraints for the machines and cutting 
parameters selection. Manufacturing industries are very 
much concerned about the quality of their products. They 
are focused on producing high quality products in time at 
minimum cost. Surface finish is one of the crucial 
performance parameters that have to be controlled within 
suitable limits for a particular process. Therefore it is 
necessary to find a suitable optimization method which can 
find optimum values of cutting parameters for minimizing 
surface roughness. ANN is found to be very useful with 
simulations tasks which have complex and explicit relation 
between control factors and result of process. Artificial 
Neural Network can be created using feed forward back 
propagation technique for simulation of the process. With 
assurance of accuracy of the predictive capabilities of the 
neural network; it may be then used for optimization.