Experimental Investigation and Analysis of Cutting Parameters in CNC Turning on Cast Iron

Abstract

In this study, the effect of the machining parameters like spindle speed, feed, depth of cut
on material removal rate is investigated .Machining is the most important of the manufacturing
processes which involves the process of removing material from a work piece in the form of
chips. Machining is necessary where tight tolerances on dimensions and finishes are required.
Being such an important process in manufacturing industry, a machining process is considered
for investigation in the present work. This paper presents the experimental investigations on the
effects of cutting variables like Spindle speed, Feed and Depth of cut on the Material removal
rate and spindle load. The experiments were conducted on Cast iron on a CNC turning machine
using ceramic insert. The experiments were conducted as per the design of experiments. Initial
trial experiments were conducted to fix the ranges for the control parameters. After conducting
the experiments the MRR measured and recorded. The effects were studied after plotting the
graphs between the Input process parameters versus the responses using Design expert software.
The results obtained in this study can by further used for optimizing the process parameters there
by the optimized results help the operator to enhance the quality as well as machining rate. The
experimental results are compared with predicted results in neural network software easy NN+,
the parameters are considered as optimized parameters for better material removal rate. . There
are several techniques available to determine the optimum values of these parameters, in this
paper machining parameters cutting speed, feed, depth of cut, are considered for optimization.
The neural networks were developed for predicting the optimized results theoretically. To
validate the results experimentally trials are then carried out a CNC turning using carbide tool by
continuous running condition under wet run on the Cast iron work piece. The predicted results
match well with experimental values. Thus proves the neural network is used for optimization of
machining parameters