Abstract: This paper is intended to develop an artificial neural network (ANN) based model of material removal rate (MRR) in the turning of ferrous and nonferrous material in a Indian small-scale industry. MRR of the formulated model was proved with the testing data and artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between inputs and output parameters during the turning of ferrous and nonferrous materials. The input parameters of this model are operator, work-piece, cutting process, cutting tool, machine and the environment.
The ANN model consists of a three layered feedforward back propagation neural network. The network is trained with pairs of independent/dependent datasets generated when machining ferrous and nonferrous material. A very good performance of the neural network, in terms of contract with experimental data, was achieved. The model may be used for the testing and forecast of the complex relationship between dependent and the independent parameters in turning operations.
Abstract: The objective of present work is to stimulate the
machining of material by electrical discharge machining (EDM) to
give effect of input parameters like discharge current (Ip), pulse on
time (Ton), pulse off time (Toff) which can bring about changes in the
output parameter, i.e. material removal rate. Experimental data was
gathered from die sinking EDM process using copper electrode and
Medium Carbon Steel (AISI 1040) as work-piece. The rules of
membership function (MF) and the degree of closeness to the
optimum value of the MMR are within the upper and lower range of
the process parameters. It was found that proposed fuzzy model is in
close agreement with the experimental results. By Intelligent, model
based design and control of EDM process parameters in this study
will help to enable dramatically decreased product and process
development cycle times.
Abstract: In this paper an attempt has been made to correlate the usefulness of electrodes made through powder metallurgy (PM) in comparison with conventional copper electrode during electric discharge machining. Experimental results are presented on electric discharge machining of AISI D2 steel in kerosene with copper tungsten (30% Cu and 70% W) tool electrode made through powder metallurgy (PM) technique and Cu electrode. An L18 (21 37) orthogonal array of Taguchi methodology was used to identify the effect of process input factors (viz. current, duty cycle and flushing pressure) on the output factors {viz. material removal rate (MRR) and surface roughness (SR)}. It was found that CuW electrode (made through PM) gives high surface finish where as the Cu electrode is better for higher material removal rate.