The utilization of Inconel 718 is increasing daily in stringent operating
conditions such as aircraft engine parts, space vehicles, chemical tanks, and
the like due to its physical properties such as maintaining strength and
corrosion resistance at higher temperature conditions. Besides, Inconel 718 is
one of the difficult materials for machining because of maintaining its strength
at elevated temperature, which generates higher cutting force leading to
observed multiple tool wear mechanisms that affect the surface quality; lower
thermal conductivity of materials produces high temperature generation that
impacts the tool performance by reducing tool life. In addition, the presence of
carbides and high hardness of IN 718 affects the machining performance.
Therefore, in this view, this article describes the effect of cutting
environments and machining parameters on the machining of Inconel 718 and
optimizes the cutting conditions for sustainable machining. Three input
parameters namely cutting speed, feed rate, and depth of cut as well as three
cutting environments such as flood cooling, MQL (minimum quantity lubrication),
and NMQL (nano minimum quantity lubrication) were considered for the
experimentation. Experimental runs were designed based on the Taguchi method,
which had a total of 27 runs performed on the CNC turning. TiAlN-coated
triangular-shaped cutting inserts were used for all experimental runs. This
research study addresses three output parameters namely surface roughness, tool
wear, and cutting temperature. Finally, the cutting condition was optimized by
using the Taguchi method and predicting the relationship between the input
parameters and the output parameter using the RSM method. Experimental results
observed that the NMQL cutting environment shows better machining performance
than the MQL and flood cooling due to the presence of nanoparticles in the base
fluid, which act as heat carriers. Whereas minimal surface roughness 0.4 μm and
lower cutting temperature (85°C) were observed at low cutting speed, feed rate,
and depth of cut (78.54 mm/min, 0.1 mm/rev, 0.1 mm) combination and minimum tool
wear was found in moderate cutting speed conditions (117.81 mm/min, 0.1 mm/rev,
0.1 mm). Whereas highest cutting temperature and tool wear such as 130°C and 0.3
mm, respectively, observed in flood cooling environment at the cutting speed
(157.08 mm/min, 0.3 mm/rev, 0.3 mm). Using the Taguchi method optimum condition
was found in the NMQL cutting environment, at the combination of cutting speed
78.54 m/min, feed 0.1 mm/rev, and depth of cut 0.1 mm. From the ANOVA results,
develop the predictive model whose results match with the experimental result.
Finally, regression model was developed between the response variable and input
parameters.
