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This paper presents the parametric study, process benefits, optimization and chip appearance of machining parameters on turning of the Inconel 718 using Nd: YAG laser source. To analyze the mentioned above effect on alloy 718, the cutting inserts of chemical vapor disposition coated (CVD) TiN/TICN/Al2O3 are used to turn at the time of machining. To evaluate the linear (mean effect plots) and interaction effect (3D surface plots) of laser parameters on the force, roughness and tool wear to keep the minimal, experiments of the L27 orthogonal array are done by selecting the controllable parameters viz speed, the rate of feed along with laser power. From the parametric study, increase in speed and laser power along with decrement in the rate of feed resulted in lower cutting force. But surface finish and tool wear reduced with a decline in speed and scale of feed and increased with increment in laser power.

Nickel based superalloys have a wide range of applications due to high mechanical strength at high temperatures, fracture toughness and resistance to corrosion. However, because of their outstanding properties, it is considered as the difficult to machine materials. Inconel alloy X-750 is used extensively in rocket-engine thrust chambers. Airframe applications include thrust reversers and hot-air ducting systems along with large pressure vessels are formed from Inconel alloy X-750. Moreover, the comparative analysis of machinability aspect using coated carbide inserts is reported few. The current study explains the machinability investigation on Inconel alloy X-750 superalloys using coated carbides. To collect the experimental data, the L16 experimental design plan is used to experiment with a machining length of 40 mm.

In the present paper, to predict the process relation between laser-assisted machining parameters and machinability characteristics, statistical models are formulated by employing surface response methodology along with artificial neural network. Machining parameters such as speed of cut; the rate of feed; along with the power of laser are taken as model input variables. For developing confidence limit in collected raw experimental data, the full factorial experimental design was applied to cutting force; surface roughness; along with flank wear. Response surface method (RSM) with the least square method is used to develop the theoretical equation. Furthermore, artificial neural network method has been done to model the laser-assisted machining process. Then, both the models (RSM and ANN) are compared for accuracy regarding root mean square error (RMSE); model predicted error (MPE) along with the coefficient of determination (R2).