Milling of Inconel 718: an experimental and integrated modeling approach for surface roughness

Abstract

Inconel 718, a hard-to-cut superalloy is reputed for having poor machining performance due to its low thermal conductivity. Consequently, the surface quality of the machined parts suffers. The surface roughness value must fall within the stringent limits to ensure the functional performance of the components used in aerospace and bioimplant applications. One doable way to enhance its machinability is the adequate dissipation of heat from the machining zone through efficient and ecofriendly cooling environment. With this perspective, an experimental and integrated green-response surface machining-based-evolutionary optimization (G-RSM-EO) approach is presented during this investigation. The results are compared with two base-line techniques: the traditional flooded approach with Hocut WS 8065 mineral oil, and the dry green approach. A Box-Behnken response surface methodology (RSM) is employed to design the milling tests considering three control parameters, i.e., cutting speed (vs), feed/flute (fz), and axial depth of cut (ap). These control parameters are used in the various experiments conducted during this research work. The parametric analysis is then accomplished through surface plots, and the analysis of variance (ANOVA) is presented to assess the effects of these control parameters. Afterwards, a multiple regression model is developed to identify the parametric relevance of vs, fz, and ap, with surface roughness (SR) as the response attribute. A residual analysis is performed to validate the statistical adequacy of the predicted model. Lastly, the surface roughness regression model is considered as the objective function of the particle swarm optimization (PSO) model to minimize the surface roughness of the machined parts. The optimized SR results are compared to the widely employed genetic algorithm (GA) and RSM-based desirability function approach (DF). The confirmatory machining tests proved that the integrated optimization approach with PSO being an evolutionary technique is more effective compared to GA and DF with respect to accuracy (0.05% error), adequacy, and processing time (3.19 min). Furthermore, the study reveals that the Mecagreen 450 biodegradable oil-enriched flooded strategy has significantly improved the milling of Inconel 718 in terms of eco-sustainability and productivity, i.e., 42.9% cost reduction in cutting fluid consumption and 73.5% improvement in surface quality compared to the traditional flooded approach and the dry green approach. Moreover, the G-RSM-EO approach presents a sustainable alternative by achieving a Ra of 0.3942 μm that is finer than a post-finishing operation used to produce close tolerance reliable components for aerospace industry.