Suleyman Yaldiz

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.

Three-dimensional finite element modeling of effect on the cutting forces of rake angle and approach angle in milling

In this study, milling operations were carried out using AISI 1040 specimens steel in dry cutting conditions. The cutting tools used in the experiment include P20 tool steel and they also have three different approach angles (45°, 60°, 75°) and rake angles (0°, −6°, −12°). In milling experiments, cutting parameters with a depth of cut of 1.5u2009mm, cutting speed of 193u2009m/min, and feed rate of 313u2009mm/min were selected. A comparison was presented between the force values which were obtained by measured value and predicted with numerical simulations, and then a good agreement was found between measured and predicted force values. As result of, it was observed that the rake and approach angles were effective in milling operations.

A comparative study of ANN and FES for predicting of cutting forces and tool tip temperature in turning

In this study, fuzzy expert system (FES) and artificial neural network (ANN) models are designed for the estimation of cutting forces in turning operations. On designed models, cutting forces and experimental temperature data obtained from different cutting conditions were used in process of turning. Cutting forces at different cutting conditions and temperature values can be estimated with the help of developed models. The results obtained with these models, compared with the experimental data. The regression values were found as 0.99505 between the Experiment-FES and, 0.9888 between Experiment-ANN in the analysis. As a result, the both artificial intelligence (AI) methods have made successful modeling, but its seen that, realized FES model has more successful results than the ANN model in the process of estimation of cutting forces.