Evaluation of Machining Variables on Machinability of Nickel Alloy Inconel 718 Using Coated Carbide Tools

Abstract

The current work was undertaken with the research aim of experimental examination of tool wear, surface roughness and burr formation during the micro-milling of Inconel 718 using different coated tools. Inconel 718 is one of the most widely used materials for purpose-oriented utilization owing to its preferred mechanical and physical properties, including high strength and corrosion resistance. On the opposite end, the machining of Inconel 718 poses certain machinability challenges, which significantly elevates tool wear and subsequently surface roughness. Cutting speed, feed rate and depth of cut were selected as variable machining inputs. With reference to tool wear, all input variables were found to be significant, with tool coating having the highest contribution ratio of 36.19%. In case of surface roughness, cutting speed and tool coating were identified as effective input parameters with contribution ratios of 51.24% and 34.27%, respectively. Similarly, depth of cut proved to be an influential factor for burr height formation (in both up-milling and down-milling), whereas feed rate had the highest contribution ratios for burr width formation during up-milling and down-milling, i.e., 39.28% and 36.26%, respectively. Consequently, contour plots for output responses were drawn between significant parameters to analyze machinability. One of the vital research outcomes was the identification of a tool coating parameter that is significant for all four analyzed aspects of burr formation. In addition, regression equations were formulated for machining responses. The best- and worst-case scenarios for individual input parameters, as identified from main effects plots, were validated during confirmatory experimentation. Moreover, effects of input variables on output response were characterized using close-up imagery, and dominant wear mechanisms were also identified. The utility of the research is underlined by the optimization of the sustainability and productivity of the manufacturing process.