Analysis of surface roughness and material removal rate in machining of AISI 1040 steel using CNC turning process

The present study investigates the effect of turning process parameters on the surface roughness and metal removal rate (MRR) of the AISI 1040 steel. Twenty-seven (L27) runs based on an orthogonal array of Taguchi techniques were performed and the grey relational analysis method was later applied to determine a most favorable turning parameters setting. Apart from this, the analysis of variance (ANOVA) was conducted to statically identify the effect of the most significant parameters. Cutting speed, feed, depth of cut, and nose radius were carefully selected as input parameters, while surface roughness and material removal rate (MRR) was output respectively. Various plots and curves have been drawn to identify the effect of various parameters on surface roughness, material removal rate, and grey relational grade. From the result, it was observed that the Taguchi-based grey relational analysis approach can be effectively used as a structured method to optimize the parameters. The results of the range analysis show that the cutting speed has the most significant effect, followed by the feed rate and depth of cut.


Introduction
In the era of universal competition, manufacturers are compelled to discover ways to improve productivity and enhance the quality of manufacturing goods. The turning process is a metal removal process from the cylindrical workpiece with the help of a cutting tool and an extensively accepted metal removal process in the manufacturing industry owing to its higher material removal rate and ability to produce good surface quality (Rizvi and Wajahat, 2010;Nalbant et al, 2007;Chowdhurya et al, 2019). AISI 1040 medium carbon steel frequently used in many industries (Mondal et al, 2013) such as fabrication industries, automobile industries, agricultural industries and many more due to its good machinability properties (Haque et al, 2017).  Table 1 consists of the literature review.

Materials and methods
The experiments were carried out using AISI 1040 steel as work piece material and its chemical composition and mechanical properties are listed in Table 2 and 3 respectively.

Design of experiments
Process parameters optimization has been widely used in the turning process. four different cutting parameters were used for tuning purposes. these were cutting speed(Vc), feed rate(Vf),depth of cut (d),and nose radius(r). Table 4 depicts the factors and their level used in this research work. The tests were conducted as per Taguchi orthogonal array L27(3 4 ), Table 5 shows the experimental result carried out in this experiment.

3.Results and Discussions Grey relation analysis
The grey relation analysis was introduced by Ju-Long (1982). Grey relation analysis optimized multiple outcomes; hence grey relation analysis is used to solve complicated problems (Asilturk and Neseli, 2012). There are three simple steps for this algorithm. In the first step, to avoid variability and different units, each response normalized. There are two options 'lower the better' and 'higher the better' for 'lower the better' Equation (1) is used and for higher the better Equation (2) is used for normalization of value. Lower-the-better condition is shown as (1) For Higher-the-better order which can be shown as (2) Where Xi (k) denotes the data sequence after pre-processing, xi (k) denotes the original sequence, the highest value of xi (k) is max xi (k), the lowest value of xi (k) is min xi (k) imply normalizing the data for lower-the-better condition is shown as A Grey relational coefficient (GRC) can be determined, as expressed in Eq. (3): ∆xi (k) is known as deviation sequence and it is the absolute value. The maximum and minimum values of ∆xi (k) are denoted by ∆min and ∆max, respectively γi denotes the Grey relational grade (GRG) of each result mentioned in table 6.value of p varies between 0 to 1. Normally it is 0.5 considered. The wk represents the normalized weight age of factor k. Table 6 shows the normalization value, grey relational analysis, grey relational grade, and Rank.

Main effect plots for GRG
The effect of various turning parameters on performance characteristics is easy to explain with the help of the main effect plot. Fig.3 show the main effect plot for Grey relational grade.  Table.7 depicts the analysis of variance (ANOVA) results for the regression model. ANOVA results show that the coefficient of determination R-square value was 55.33%, and R-(adj) was 31.15%, which indicated the model's good compatibility with experimental results. Trial No. 24 was identified as the optimal trade-off having combination (N = 800 rev/min, f = 01mm, Depth of cut = 2 mm, Nose radius = 0.4 mm) and can be verified from Fig.4 as having the highest GRG value.

Analysis of Variance (ANOVA) table for GRG
The basic function of ANOVA is to identify the machining parameters that notably affect the S/N ratio. Also; it can be applied to resolve the contribution of the change of a cutting parameter.  Fig.5 shows the interaction plot for cutting speed, Feed, depth of cut, and nose radius. It seems that there is an interaction effect present between the input variables The regression plot obtained during the generation of multiple regression models for the grey relational grade is shown in fig 6. The normal probability curve of the residual for the GRG shows a straight line with the major amount of points lying on the lower side of the good fitted line. Residual Versus fits curve depicts that the residual is scattered randomly about the zero points. The distribution of the residuals for all observations is represented by a Histogram. From versus order curve Tenth and twenty-one sets of residuals are low.

Interaction plot
3D surface plot for surface roughness 3D Surface plots of Ra vs. Various combinations of machining parameters are shown in Fig. 7a, 7b, and 7c. Fig.7a depicts the influences of feed rate (f) and depth of cut (Doc) on the surface roughness, while the feed is placed at the middle point. Fig.7 b depicts the estimated response surface in relation to the cutting speed (Vc) and depth of cut (Doc). The effects of the cutting speed and feed rate (f) on the surface roughness are shown in Fig.7c. These 3D plots verify the notes revealed during the principal effects plots analysis (Bhemunia and Chalamalasetti, 2014).  Fig. 8a, 8b, and 8c. Fig.8a depicts the influences of the depth of cut (Doc) and feed rate (f) on the material removal rate (MRR), while the cutting speed is placed at the middle point. Fig.8 b depicts the estimated response surface in relation to the cutting speed (Vc) and depth of cut (Doc). The effects of the cutting speed and feed rate (f) on the MRR are shown in Fig.8 c. These 3D plots verify the notes revealed during the principal effects plots analysis (Bhemunia and Chalamalasetti, 2014).

Conclusion
In this present study, A novel method was adapted to determine the effect of turning of AISI 1040 parameters on output. A single objective function and a multi-objective function known Taguchi based grey relational analysis were selected to find the optimal combination of the input process parameters, for lowering the surface roughness and to achieve the higher metal removal rate in turning operation. 1. It is observed that simultaneous improvement in multiple responses of machining parameters in turning process is enhanced by using the integration of grey relational grade with the Taguchi technique. 2. Experimental results shows that a cutting speed of 800 rpm, feed rate of 0.1 mm/rev, depth of cut 1.6 mm, and nose radius of 1.2 mm provide the optimum parametric optimum condition by grey relational analysis. 3. From ANOVA result, cutting speed is the most dominate parameter contributing by 23.9%, while feed rate has an effect of 17.5% and depth of cut by 1.04%.