Investigations on Machining Performances and Tool Wear Surface Characterization During CNC Hard Turning of AISI 4140 Alloy Steel

Researchers are always trying to explore, use resources so that the machining performances become more efficient, economically viable, and environment-friendly which are in general, conflicting in nature. In this investigation, surface finish, material removal rate, and tool wear are considered as responses, and cutting speed, feed, and depth of cut are considered as input factors for optimization of AISI 4140 alloy steel during CNC dry turning. In most of the previous researches, weights of the responses are considered arbitrarily. But, in this investigation, weighted ratio analysis is considered as an optimizing tool whose weights are determined by the Eigenvectors obtained from the principal component analysis of the experimental data. This weighted ratio analysis technique requires very few computations compared to others, and even it can be performed without a computer. Surface texture of the tool wear is measured using image processing of the wear surface. First, a metallurgical microscope captures an image of the sharp tool (unworn), and RGB values of the gray scale image are noted, which acts as a reference. After machining, RGB values of the wear surface from the gray scale image are noted. Comparing these two RGB values, the amount of flank wear of the tool is determined. It is observed that the error is within 10% and therefore it is accepted.