Prediction of surface roughness using artificial neural network in lathe

In this study, the effect of tool geometry on surface roughness has been investigated in universal lathe. Machining process has been carried out on AISI 1040 steel in dry cutting condition using various insert geometry at depth of cut off 0.5 mm. At the end of the cutting operation, surface roughness has been measured using MAHR M1 perthometer. After experimental study, to predict the surface roughness, an ANN has been modelled using the data obtained. Modelling of ANN; tool nose radius (r), approach angle (K), rake angle (Y), tool overhang (L) have been used. In this study, surface roughness (Ra) is output data. The ANN has been designed on PC by using Matlab 6.5 software. Comparison of the experimental data and ANN results by means of statistically t test show that there is no significant difference and ANN has been used confidently.