Experimental Investigation on Thermally Assisted Machining of Inconel 718 Superalloy

Abstract

Nickel-based superalloys are widely used in the manufacture of various components and structures for aerospace, marine and nuclear power generation, chemical, petrochemical and process industries. Accounting for about 50 % weight of materials used in an aerospace engine, mainly in the gas turbine components. Aircraft engine constitute total materials about 30 % of nickel-based superalloys. These alloys are also used in structural material of various parts in the main engine like cryogenic tanks and in pressure vessels of nickel hydrogen batteries used in aviation. Nickel based superalloys desirable mechanical properties like high fatigue strength, high yield and ultimate strength, corrosion and oxidation resistance over a wide temperature range of -217 oC to 700 oC. Generally, superalloys are challenging to machining due to their peculiar characteristics such as low thermal conductivity, high hot hardness, high work hardening tendency, chemical affinity towards cutting tool and hard abrasive carbide particles present in the microstructure. Due, to these limitations nickel based superalloys are very difficult to machine. Conventional machining of nickel based superalloys exhibits poor machining performance due to high chemical affinity, strong work-hardening, and tendency to abrasive nature. To overcome these problems, thermally assisted machining of difficult to cut materials is an emerging technique; it is an alternative of conventional machining. In present investigations, thermally assisted machining characteristics of Inconel 718 superalloy were studied. The most significant parameters and suitable cutting tool to machine Inconel 718 have been identified. The effect of process parameters on the cutting forces, surface roughness, tool wear, material removal rate, surface topography, heat affected zone (HAZ), microhardness, phase changes and residual stresses were investigated. The analysis of variance (ANOVA) was performed to find out the contribution of process parameters on the output responses and the regression equation has been developed using Minitab 17 software. L27 orthogonal array of experiments was conducted on Inconel 718 by using uncoated carbide, TiAlN nano multilayer, TiCN/Al2O3/TiN triple layer coated with considering process parameters of cutting speed, feed rate, depth of cut and workpiece temperature. Experimental results showsthat cutting speed, feed rate depth of cut and workpiece temperature are the most significant process parameters on cutting force; feed rate and workpiece temperature are most significant process parameters for surface roughness; cutting speed, feed rate and depth of cut are most significant process parameters for tool wear and metal removal rate. In extensive experimentation and analysis TiAlN multilayer coated tool was found to be appropriate to machine Inconel 718 superalloy than uncoated and TiCN/Al2O3/TiN coated tool. Cutting forces in TAM were observed to be less compared to conventional machining; Surface roughness was improved remarkably in TAM by about 21.8 %. Tool wear was reduced by 29 %, and MRR increased by 5.9 % in TAM compared to conventional machining. However, the individual effects of cutting speed, feed rate, depth of cut and workpiece temperature on performance characteristics like cutting forces, surface roughness, metal removal rate, tool wear, heat affected zone, microhardness, surface topography, residual stresses and phase changes on the machined surface were analyzed. The cutting forces and surface roughness decrease as the cutting speed and workpiece temperature increase, but increases with increase in feed rate and depth of cut. The microhardness increases as the workpiece temperature increases, hence TAM improves the performance of the product. Thermally assisted machining reveals that there are neither phase changes, nor broadening of the peaks that were observed at different machining conditions. Tensile residual stresses are induced on the machined surface the workpiece temperature increases the residual stresses were decreased. Central composite rotatable design of experiments was conducted to evaluate the cutting force, surface roughness, tool wear and metal removal rate. Based on the experimental results corresponding second order regression models have been developed. The developed regression models were utilized as fitness functions in particle swarm optimization algorithm (PSO). The lower and upper levels of process parameter were selected as constraints for the optimization. The simultaneous optimization of process parameters to minimize cutting force, surface roughness, and tool wear and to maximize MRR were performed by using particle swarm optimization algorithm. The optimized results were cross checked by conducting the confirmation experiments and the predictedvalues found to have good agreement with the experimental values. PSO can also be used as a tool to optimize and predict the results during TAM machining of Inconel 718