Experimental Investigation to Optimize Tool Life and Surface Roughness in Inconel 718 Machining

Abstract

In addition to the cutting conditions, the surface quality is also affected significantly by a worn tool in machining processes. Identification of the desirable tool life so that the surface quality is maintained within a desirable level is an essential task, especially in the machining of hard materials. In this paper, an optimal tool life and surface quality were identified in the turning operation of Inconel 718 Superalloy by means of experimental investigations and intelligent methods. First, the effect of machining time (MT) at the different cutting parameters was widely investigated on the surface roughness using the neural network model. Then, the modified Non-dominated Sorting Genetic Algorithm (NSGA) was implemented to optimize tool life and surface roughness. For this purpose, a new approach was implemented and the MT was taken into account as the input and output parameters during the optimization. Finally, the results of optimization were classified and the suitable states of the machining outputs were found. The results indicate that the implemented strategy in this paper provides an efficient approach to determine a desirable criterion for tool life estimation in machining processes.