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Abstract:In this paper, an experimental study was conducted to determine the effect of different cutting parameters such as cutting speed, feed rate, and depth of cut on cutting force, surface roughness, and tool wear in the milling of Ti-6242S alloy using the cemented carbide (WC) end mills with a 10 mm diameter. Data obtained from experiments were defined both Artificial Neural Network (ANN) and Response Surface Methodology (RSM). ANN trained network using Levenberg-Marquardt (LM) and weights were trained. On the other hand, the mathematical models in RSM were created applying Box Behnken design. Values obtained from the ANN and the RSM was found to be very close to the data obtained from experimental studies. The lowest cutting force and surface roughness were obtained at high cutting speeds and low feed rate and depth of cut. The minimum tool wear was obtained at low cutting speed, feed rate, and depth of cut.

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WC tools was observed to be less than the CVD coated WC tools. However, the values of the surface roughness obtained with PVD coated WC tools with increase in depth of cut, feed rate and cutting length has given us higher values when compared to CVD coated WC tools.