Experimental Investigation of Dimensional Accuracy During Micro-Machining of Inconel-718 Sheet

Abstract

The traditional machining method is incapable of machine the Inconel-718 material due to its lower thermal properties, lower elastic properties and high chemical conductive at expanding temperature. The Nd: YAG laser cutting process provides better cutting quality, precision and geometric accuracy in shape or size. But due to high thermal properties, it is difficult to remove completely undesirable factors like the Top kerf width (TKW), Bottom kerf width (BKW) and kerf taper (KT). To improve the cutting quality of Inconel-718, experiments have been performed on a 300W pulsed Nd: YAG laser cutting system at various levels of input cutting parameters. Thereafter suitable artificial neural network has been adopted to develop mathematical models in terms of the aforementioned input cutting parameters for geometrical quality characteristics: Top Kerf Width, Bottom Kerf Width, and Kerf Taper. These developed models have been validated by comparing the predicted values with the experimental ones. Further, these models have been used to explore the dependency of input parameters on the responses and to predict a safe cutting range. At last, more experiments have been performed to validate the identified range. From the results, it has been found that the obtained optimal range of cutting parameters is significant.

Keywords: Artificial neural network, Dimensional accuracy, Inconel-718, Micro-machining.

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