The improved productivity, accuracy, and the decreased cost of manufacturing processes is the prime objective of the engineering industry (Boothroyd, 1994; Mehrabi et al., 2000; Ernst & Kim, 2002). The practice of machining is the basis of the engineering industry and involved in each product either directly or indirectly in the present civilization (Schreiber et al., 2000; Groover, 2007; Lieder & Rashid, 2016). The machining is the metal cutting process in which machining allowance is removed from the workpiece to form the required size, shape, and surface finish of the product (Rajurkar et al.,1999; El-Hofy, 2013; Selvam & Senthil, 2016). The metal cutting processes are turning, boring, drilling, reaming, milling, planning and shaping, threading and tapping, grinding, burnishing and deburring (Todd et al., 1994; Youssef & El-Hofy, 2008; Hameed et al., 2012; Selvam & Sivaram, 2017, 2018; Selvam & Meji, 2018).

Among them, in nineteenth-century, the grinding process is developed as a metal cutting process (Selvam et al., 2012, 2014; McCoy, 2017; Iuliano et al., 2019). In the mid of the twentieth century, it was realized that the grinding process is the high technology process in the manufacturing of aero engines, Internal Combustion engines, missiles, bearings, microelectronic devices, transmission, and astronomical instruments and identified as a key to achieve desired quality (Dhar et al., 2006; Ponnusamy et al., 2018).

The grinding is the abrasive machining process employed in finishing operation of the workpiece for high accuracy, low material removal, and high surface finish of hard materials with close dimensional tolerances (Ezugwu, 2005). The cylindrical grinding is the most vital and basic type of cylindrical grinding process in which the workpiece is rotated by work head of the machine, held between work head and tailstock centres (Tönshoff et al., 1998; Altintas & Weck, 2004). The grinding wheel approaches the workpiece automatically during the transverse acting of the rotating workpiece during traverse grinding and in plunge grinding, the rotating workpiece is kept stationary without table traverse (Bennett & May, 1966; Karpuschewski et al., 2008). The improved productivity in machining concerned depends on the higher material removal rate with higher surface finish achieved through the correct choice of process parameters which require in-depth knowledge on wheel and machine cutting parameters (Scott et al.,1991; Jahan et al., 2011). The cylindrical grinding is finish machining operation to achieve a good surface finish with close dimensional tolerances. The engineering materials with their respective heat-treated condition are selected which are used extensively as the main spindle for grinding machines, drilling machines, and lathes so on. The plunge and traverse grinding process are the two different cylindrical grinding process used in this Taguchi optimization process. The choice of the cylindrical grinding process parameters depend on wheel parameters and the machine cutting parameters which are explained by any number of researchers. The wheel parameters, such as: type of abrasive, grain size, type of bond, grade of the wheel, structure of the wheel, and machine cutting parameters, such as: speed of the job, Infeed, traverse rate, wheel speed play an important role in getting optimized value of material removal rate and surface finish (Jawahir et al., 2011; Debnath et al., 2014).

The parameter design approach of Taguchi's method is applied for the optimization of cylindrical grinding process parameters (Neşeli et al.,2012; Selvam & Senthil, 2016; Selvam et al., 2017). In Taguchi method, Orthogonal Array (OA) is used to design the experiments. Signal to Noise (S/N) and raw data analysis are used to evaluate the impact of grinding process parameters on Material Removal Rate (MRR), the Analysis of Variance (ANOVA) is used to evaluate the quality of the process (Jadoun et al., 2006, 2009; Gijo et al., 2011; Habib, 2014; Kumar et al., 2015).

Many researchers have found different methods to reveal the effect of the cylindrical grinding process parameter on the material removal rate and surface roughness.

Kumar et al. (2012) have conducted a study on external cylindrical grinding process parameters and its effects on Mild steel round bars using the Taguchi method. Three levels were selected for both variables depth of cut and cutting speed in the experimental study. L₉ orthogonal array is selected and the trials during experimentation were repeated thrice to perform 27 experiments. Optimal conditions of variables and their effect on the optimal material removal rate are the prime objective of this study. The variables are optimized by signal to noise ratio (S/N) and those are investigated by Analysis of Variance (ANOVA) to find the parameters affects the quality characteristics considerably.

1.1 Experimental Setup

Work Material:

• Mild steel round tube having 29.1 mm outer diameter and 50.5 cm long.

Experimental Factors and Levels:

• Variables - Depth of cut (mm) and cutting speed (m/min)
• Levels - 3 levels for each variable
• Orthogonal Array – L₉ orthogonal array is repeated thrice to get 27 experiments
• Optimal Parameter - Metal removal rate

External Cylindrical Grinding Variables:

• Cutting Speeds - 41.58 m/min, 41.07 m/min, and 57.63 m/min.
• Depth of Cut - 10 μm, 15 μm, and 20 μm.

1.1.1 Inference

After experimentation study, the optimal grinding conditions by S/N ratio analysis are cutting speed of 41.07 m/min and depth of cut of 20 μm for the optimal material removal of 19.906 mm³/s.

The significant contribution of variables towards optimal material removal is cutting speed 47.30% than that of 4.40% of the depth of cut.

Jeevanantham et al. (2017) have conducted a study on process optimization on internal grinding on hollow round C40 E steel bar using Taguchi method. The selected materials are used extensively in manufacturing machine elements, such as shafts, axles, spindles, studs, etc.,

The input process variables for this experiment are cutting force, cutting speed, and depth of cut work and the output parameter, i.e surface roughness was optimized using ANOVA.

1.2 Experimental Setup

Work material:

• C40 E round steel tube having an outer diameter 100mm, internal diameter 50 mm with 50 mm length.

Experimental Factors and Levels:

• Variables - Cutting force (N), cutting speed (m/min) and depth of cut (mm)
• Samples - Sample A and Sample B
• Levels - 2 levels for each variable
• Optimal Parameter - surface finish

Internal Cylindrical Grinding Variables:

• Cutting Force - 47.2 N, 81.6 N and 96.3 N
• Cutting Speeds - 45.1 m/min, 79.3 m/min and 102.7 m/min.
• Depth of Cut - 0.1 mm, 0.15 mm and 0.20 mm

1.2.1Inference

After experimentation study, the experimental value of surface roughness (0.524 μm) was compared with the predicted surface roughness derived from the S/N ratio (0.517 μm). The error between the experimental and predicted surface roughness is about 1.3%, hence the experimental work found to be satisfactory.

The cutting force and the cutting speed have shown the predominant effect on the surface finish whereas the depth of cut has a minimum effect towards surface roughness.

The optimum grinding conditions for the minimum surface roughness of 0.524 μm are cutting force of 47.2 N, cutting speed 0.0084 m/min and depth of cut of 0.10 mm.

Mohite et al. (2017) has conducted the experiment on external cylindrical grinding process parameter optimization on EN 19 steel material using Taguchi optimization method, the preliminary experiments were conducted for the process variables such as spindle speed, depth of cut, feed rate, and hardness. The nonlinearity effect of all process variables was examined at three levels. Linear, nonlinear, linear with interaction, linear with square, a quadratic mathematical model was formed to get correlation among the selected process variables. The Material Removal Rate (MRR) and surface roughness are the response of the mathematical model used for the experimental study. The Taguchi L₉ orthogonal array was selected for the predicted experimental study to get the optimal choice of process parameters by Signal to Noise ratio (S/N). The mathematical experimental study was compared with Taguchi predicted experimental study.

1.3 Experimental setup

Work material:

• Diameter 25 mm, 120 mm long having varies hardness value of EN19 steel round bar selected. The varies hardness of the selected material was achieved through the heat treatment process.

Experimental Factors and Levels:

• Process variables - spindle speed, depth of cut, feed rate, material hardness.
• Samples - 9 samples
• Levels - 3 levels for each variable
• Orthogonal Array –L₉ orthogonal array

External Cylindrical Grinding Variables:

• The Hardness of the Material - 30 HRC, 40 HRC, 50 HRC.
• Spindle Speeds- 145 rpm, 247 rpm, and 415 rpm.
• Depth of Cut - 20 μm, 30 μm, and 40 μm
• Feed Rate - 0.06 mm/rev, 0.12 mm/rev, 0.18 mm/rev
• Wheel Speed - 3600 rpm
• Grinding Wheel - SiC
• Coolant - Soluble oil.

1.3.1 Inference

The material removal rate obtained through the mathematical model was 2.0177 g/s and it was compared with optimal results of Taguchi model and the variation was 0.049%. The surface roughness obtained through the mathematical model was 0.4229 mm and it was compared with optimal results of Taguchi model and the variation was 2.62%. The optimum process variables for minimum surface roughness value was obtained by the material with a hardness of 50 HRC, depth of cut 20 μm, 0.06 mm/rev of feed rate with a wheel spindle speed of 145 rpm. The optimum process variables for maximum material removal value obtained by the material with a hardness of 30 HRC, depth of cut 40 μm, 0.18 mm/rev of feed rate with a wheel spindle speed of 247 rpm. Both linear and non-linear model identified to be better among all other mathematical models when compared with Taguchi predicted experimental model. The hardness of the material is the predominant factor to get minimum surface roughness with a higher hardness value of the selected material and to get Maximum Material removal rate with lower harness value of the selected material.

Siddik et al. (2017) focused on material removal rate and surface roughness to study the effect of process variables and to optimize the process variables in cylindrical grinding using Inconel 718 superalloy. Inconel 718 material is used generously in a nuclear plant, petroleum plant, and aerospace manufacturing industries. It has got higher thermal stability, hard to machining, greater tensile, and hardness properties. MRR is the rate of material removal and the surface roughness is the roughness of deviation of a machined surface from the reference surface. Since grinding is the costlier operation, the success of grinding depends on optimizing the process variables. Statistical approach of the Taguchi method is used for the optimization study. L₉ orthogonal array is used to design the experiments and S/N ratio which determines the variation of output parameters. This paper shows the effect of work speed and table traverse is getting a higher material removal rate and minimum surface roughness.

1.4 Experimental Setup

Work Material:

• Inconel 718 50% nickel having high-temperature resistance ranging from - 220 ^oC to 780 ^oC, higher strength, minimal ductility, higher resistance against oxidation and corrosion.

Experimental Factors and Levels:

• Process Variables - Work speed, depth of cut, feed rate, Wheel speed.
• Samples - 9 samples
• Levels - 3 levels for each variable
• Orthogonal Array – L₉ orthogonal array

External Cylindrical Grinding Variables:

• Work Speeds - 5 m/min, 10 m/min and 20 m/min.
• Depth of Cut -10 μm, 20 μm, 30 μm
• Feed Rate - 3 m/min, 6 m/min.
• Wheel Speed -1800 rpm
• Grinding Wheel - Aluminium oxide grade A60 N S V10 C

1.4.1 Inference

After conducting nine experiments, the choice of the grinding parameters arrived as following.

Surface roughness and Material removal rate influenced by work speed and feed rate, respectively. However, material removal rate does not depend on work speed. In fact, it solely depends on the feed rate.

From L₉ experiments, experiment number 4 achieved the least surface roughness value and experiment number 2 and 4 achieved the higher material removal rate.

The minimum surface roughness is obtained through 10m/min of work speed, 6 m/min of feed rate, 10 mm of the depth of cut and 1.43 l/min of coolant flow rate.

The maximum material removal rate is obtained through 6m/min of feed rate, 0.03 mm of the depth of cut, and 3.33 l/min of coolant flow rate.

Kumar and Bhatia (2015) have optimized material removal rate and surface roughness by the statistical method of Taguchi optimization study for the EN15 AM free cutting steel used extensively in automobiles. EN15 AM steel has a character of higher machinability and it uses mass production components like a shaft, connecting rod, and spindles in automobiles. Even though the volume of stock removal from the component is very small to get higher surface roughness with close tolerances in cylindrical grinding machine, the requirement of optimizing the cylindrical grinding process parameters is a need in this present scenario to get maximum material removal with minimal surface roughness so as to have the higher life of the component.

To conduct optimization study L₉ orthogonal array with four process variables each containing three levels are used as an input process parameters. Minitab 17 software with Analysis of variance analyses the MRR.

Based on the S/N ratio, the optimized value for the Material removal rate was found.

1.5 Experimental Setup

Work Material:

• EN15 AM steel having 30 mm diameter and 380 mm length. The workpiece cut into 126.7 mm long and turned to 28.5 mm diameter. GG600 model grinding machine has been used for this study.

Experimental Factors and Levels:

• Process Variables - Work speed, depth of cut, feed rate, Wheel speed.
• Samples -18 samples
• Levels - 3 levels for each variable
• Orthogonal Array – L₁₈ orthogonal array

External Cylindrical Grinding Variables:

• Work Speeds- 80 rpm, 155 rpm, 324 rpm.
• Depth of Cut – 20 μm, 40 μm, 60 μm
• Feed Rate - 100 mm/min, 175 mm/min, 275 mm/min.
• Wheel Speed -1800 rpm, 2000 rpm

1.5.1 Inference

Except for depth of cut, all other process variables, such as work speed, feed rate, and the wheel speed has a significant effect on the Material removal rate. Wheel speed 14.85% of contribution and the speed is 1800 rpm, Work speed 38.95% of contribution and speed of work 155 rpm, feed rate 12.85% of contribution and feed rate of 275 mm/rev and depth of cut 9.8% of contribution and 0.04 mm is the percentage of contribution and the optimal process values to achieve the higher material removal rate in the cylindrical grinding.

Karande et al. (2017) improved the surface finish grinding, which is the most important machining process among all other processes. Higher Material rate and good surface finish are the two important outcomes considered in this study. The work speed, table traverse, and the depth of cut are only considered for this optimization study. The other parameters, such as wheel grade, material properties and hardness, the quantity of coolant flow have not been considered in this study. The selected EN19 steel material used in general engineering and automobile industries extensively. To identify the relationship between process variables and surface roughness, material removal rate L₉ orthogonal array was used. The significance of the process variables is analyzed by analysis of variance with the experimental results.

1.6 Experimental Setup

Work Material:

• EN 19 steel having different hardness value is used for this study.

Experimental Factors and Levels:

• Process Variables - Work speed, depth of cut, feed rate, Material hardness.
• Samples - 9 samples
• Levels - 3 levels for each variable
• Orthogonal Array – L₉ orthogonal array

External Cylindrical Grinding Variables:

• The hardness of the Material - 30 HRC, 40 HRC, 50 HRC.
• Spindle Speeds - 145 rpm, 247 rpm, and 415 rpm.
• Depth of Cut -20 μm, 30 μm, and 40 μmm
• Feed Rate - 0.06 mm/rev, 0.12 mm/rev, 0.18 mm/rev
• Grinding Wheel - Silicon carbide.

1.6.1 Inference

From the above study, 35.17% contribution of the hardness of the material and 20.15% contribution of feed rate are involved in minimum surface roughness.

The optimum process variables for minimum surface roughness was obtained by the material with a hardness of 50 Hrc, depth of cut 20 μm, 0.18 mm/rev of feed rate with a wheel spindle speed of 415 rpm.

Mekala et al. (2014) stated that AISI-316 Austenitic Stainless Steel is found in most of the aerospace, automobile, applications. Cutting speed of the wheel, feed rate, and depth of cut are used in this optimization study as input process parameters. This paper focuses on how empirical and physical model explains the different characteristics of the centreless cylindrical grinding process. The Taguchi method, Analysis of variance, and regression analysis are to optimize the machining process variable to achieve maximum material removal rate and minimum surface roughness. Because of the following problems, namely poor chip breaking, high work hardenability, machining distortion, minimum crack tendency, the conventional method of finding out the optimum process parameters will be very much difficult. L₉ orthogonal with 3 levels for each variable has been used to get maximum material removal without occurrence of the above problem without scarifying the surface quality has been discussed.

1.7 Experimental Setup

Work Material:

• AISI-316 Austenitic Stainless Steel round bar of 50 mm diameter and 70 mm long has been selected for this study.

Experimental Factors and Levels:

• Process Variables - Work speed, depth of cut, feed rate.
• Samples - 9
• Levels - 3 levels for each variable
• Orthogonal Array – L₉ orthogonal array

Centreless Cylindrical Grinding Variables:

• Cutting Speeds - 560 m/min, 780 m/min, 1000 m/min.
• Depth of Cut - 3 μm, 4 μm, 5 μm.
• Feed Rate - 0.073 mm/rev, 0.093 mm/rev, 0.130 mm/rev

1.7.1. Inference

The maximum material rate of the AISI-316 Austenitic SS is obtained by the following value of process variables, namely 560 m/min of cutting speed of the wheel, 0.130 mm/rev of the work, and 0.005 mm of the depth of cut without affecting the surface quality. The optimized process variable furnishes the maximum material removal achieved through overcoming poor chip breaking, high work hardenability, machining distortion, and minimum crack tendency.

Sridhar and Khan (2014) explain that material removal rate and surface finish with dimensional accuracy are the two important expectation of any manufacturing industry. Grinding is the metal finishing process which gives the component a good surface finish. Among work speed, wheel speed wheel grain size, number of passes, the cutting speed and feed are the two important process variables which implies a severe effect on the material removal rate and higher surface. Taguchi method is used to optimize the process parameters for grinding of EN21AM material, namely cutting speed, feed, and depth of cut. L₉ orthogonal array with three variables each at three levels is used to find the optimized process variables for EN21AM steel.

The model for grinding process variables and their responses are required to achieve predictive responses like surface roughness and material removal rate. The Taguchi method with Analysis of variance is used to describe the importance of process variables. The S/N ratio is used to find the optimize the process variables.

1.8 Experimental Setup

Work Material:

• EN21AM tool steel

Experimental Factors and Levels:

• Process Variables - Cutting speed, depth of cut, feed rate.
• Samples - 9 samples
• Levels - 3 levels for each variable
• Orthogonal Array -L₉ orthogonal array

External Cylindrical Grinding Variables:

• Cutting Speeds - 1000 rpm, 700 rpm and 500 rpm.
• Feed Rate - 0.075 mm/min, 0.095 mm/min, 0.120 mm/min.
• Depth of Cut – 2 μm, 3 μm, 4 μm

1.8.1 Inference

After completing the L₉ experiments, the maximum material removal rate and higher surface finish are achieved through the following optimized process variables.

The maximum surface finishes achieved through 1000 rpm of cutting speed, 0.12 mm/rev of feed rate, and 4 μm of the depth of cut.

The higher material removal rate is obtained through 1000 rpm of cutting speed, 0.12 mm/rev of feed rate, and 4 μm of the depth of cut.

Panthangi and Naduvinamani (2017) stated that grinding is a finish machining process to obtain a higher surface finish with close dimensional tolerance. Both internal and external diameter of the cylindrical work can be finished by cylindrical grinding. The component can be ground to any shape and size by both Plunge and traverse cylindrical grinding processes. The process variables for the cylindrical grinding process are work speed, wheel speed, table traverse, the hardness of the work, depth of cut, the grade of the wheel, and coolant conditions. The depths of cut, work speed and hardness of the work have been considered for this study to evaluate the surface roughness. Genetic algorithm and Taguchi optimization are used in this study and using L₉ orthogonal array, the experiments were conducted. Taguchi method found the ratio between desired values to undesired value as an S/N ratio to determine the quality characteristics. To find out the optimization process variables by genetic algorithm, the MATLAB software is being used.

1.9 Experimental Setup

Work Material:

• EN19 having higher ductility, resistance to shock and wear used for manufacturing gears pinion, shafts, and spindles. EN24 having extended machinability, ductility, higher tensile strength with resistance to shock and wear used for heavy-duty shafts, gears, studs, and bolts. EN31 having higher hardness with compressive strength and resistance to abrade used for needle rollers.

Experimental Factors and Levels:

• Process Variables - work speed, depth of cut, material hardness.
• Samples - 9 samples
• Levels - 3 levels for each variable
• Orthogonal Array – L₉ orthogonal array

External Cylindrical Grinding Variables:

• Work Speeds- 100 rpm, 214 rpm and 340 rpm.
• Depth of Cut - 1 μm, 2 μm, 3 μm
• Material Hardness - 40 HRC, 47 HRC, 55 HRC

1.9.1 Inference,/h3>

As per the Taguchi method, predicted S/N ratio value is 3.00873 and subsequent predicted surface roughness value is 0.707 μm.

The surface roughness value for the experimental study based on optimized process variables and values such as Work speed 214 rpm, depth of cut 1 μm, and material hardness of 40 HRc.

From the study, it is found that harness with 45% contribution involves in minimization of surface roughness. But the depth of cut 20% of contribution and work speed 7.74% of contribution affects the surface finish.

The predicted surface finish 0.707 μm at 95% of process consistency level has been obtained using the Taguchi method.

Analysis of variance using regression equation showed the importance of hardness in attaining minimum surface roughness.

The minimum surface roughness for the following process variables found from the genetic algorithm are work speed of 100 rpm, depth of cut of 1 mm, and material hardness of 40 HRc obtained is 0.6957 μm

Thakor and Patel (2014) have used EN8 steel in this study of optimization of grinding process variable. As far as quantity and quality are concerned, the MRR and surface roughness are the two significant outcomes in manufacturing industries. Even though the depth of cut, type of cutting fluid, work speed, wheel grade, wheel speed significantly affects the MRR and surface roughness, Except type of cutting fluid, work speed and depth of cut are only used in this study all other variables are kept constant. Using regression analysis, the full factorial method of experiments has been conducted. The three process variables are considered namely type of cutting fluid, work speed, and depth of cut. The regression analysis is used to find out the optimum process variable, whereas the S/N ratio is used to determine the quality characteristics.

1.10 Experimental Setup

Work Material:

• EN8 steel having 22 mm diameter and 150 mm long. The following are properties of the material: hardness of 248-302 BHN, 700-850 N/mm² tensile stress, 465 N/mm², and elongation of 16%

Experimental Factors and Levels:

• Process Variables - Type of cutting fluid, depth of cut, work speed.
• Samples - 27 samples
• Levels - 3 levels for each variable
• Orthogonal Array – L₂₇ orthogonal array

External Cylindrical Grinding Variables:

• Work Speeds - 60 rpm, 75 rpm, 120 rpm.
• Depth of Cut - 300 mm, 400 mm, 500 mm
• Cutting Fluids - Water soluble oil, pure oil, pure water.

1.10.1 Inference

From the above optimization study, the following results have been obtained. The water-soluble oil was found suitable for obtaining 0.4246 μm surface roughnesses with the optimized process variables of 120 rpm of work speed and 500 μm of the depth of cut. In this study, it is found that higher work speed and depth of cut implies the higher surface finish when water-soluble oil is used as cutting fluid. Water-soluble oil with extended flowability and medium viscosity imply minimal surface roughness, whereas pure oil has low flowability and extended viscosity and pure water with extended flowability and low viscosity have not fetched the minimum value of surface roughness. The higher material removal rate is obtained with higher work speed due to an increase in rubbing action of abrasive grain and higher depth of cut due to an increase in chip thickness which results in minimal surface roughness.

Jagtap et al. (2011) used CNC cylindrical grinding machine to find out the optimum process variables, such as work speed, depth of cut, and feed rate during grinding of OHNS steel to maintain quality in terms of surface roughness and higher production in terms of material removal rate. Response surface methodology based mathematical model was developed to examine the surface roughness. This mathematical model is used to find the soundness of the above process variables and power of this mathematical model is evaluated by Analysis of Variance (ANOVA). The study has been carried over based on the mathematical model for the process parameters against surface roughness. Finally, the optimized parameters found based on a mathematical model has been re-examined by conducting experiments.

1.11 Experimental Setup

Work Material:

• OHNS steel having 25 mm diameter and 120 mm long, hardness 60 HRC. The following are properties of the material: hardness of 288 BHN, 950 N/mm2 tensile stress, 465 N/mm2 yield stress, elongation of 10%, and 25 Nm Impact strength.

Experimental Factors and Levels:

• Process Variables – Spindle speed, depth of cut, feed.
• Samples - 20 samples
• Levels - 3 levels for each variable

External Cylindrical Grinding Variables:

• Work Speeds - 200 rpm, 250 rpm, 300 rpm.
• Depth of Cut – 5 μm, 10 μm,15 μm
• Constant Parameters – Wheel speed, wheel grade, workpiece diameter, and number of passes.

1.11.1 Inference

The MINITAB 14 software has been used to find the optimized process parameters. From this study, it is found that the Material removal rate and surface finish is being influenced by the depth of cut and feed rate. Owing to the increase of depth of cut as well as wheel speed; there will be an increase of Material removal rate that leads to an increase in the dullness of the grit which in turn increases the grinding force.

The geometry of the grinding surface is affected by the dullness of the grit, which leads to excess heat, burning mark and in turn produces micro-cracks. Hence the process variables have to be selected within the capacity of the selected machine.

Rudrapati et al. (2013) have used grey based Taguchi optimization study using L₉ orthogonal array to find out the influence of grinding parameters on stainless steel to obtain solutions to multi-objective problems. The life of the machined component can be increased by decreasing the friction and wear during the function. Longitudinal feed was the significant influential grinding parameters which affect the surface roughness considerably. The grinding process variables have been evaluated by the S/N ratio. This study is used to estimate the optimum process variables during cylindrical traverse grinding of stainless steel. The root means square roughness value (R_q), as well as the arithmetic average height parameter value (R_a), is used to describe the quality of the machined component.

1.12 Experimental Setup

Work Material:

• Stainless steel having 38 mm diameter and 50 mm long. Experimental Factors and Levels:
• Process Variables – Work speed, Infeed, longitudinal feed.
• Samples - 9 samples
• Levels - 3 levels for each variable
• Orthogonal Array – L₉ orthogonal array

External Cylindrical Grinding Variables:

• Work Speeds - 80 rpm, 112 rpm, 160 rpm.
• Traverse Feed – 70 mm/s, 80 mm/s, 90 mm/s
• Depth of Cut – 4 μm/cycle, 0.05 μm/cycle, 0.06 μm/cycle

1.12.1 Inference

The MINITAB 16.1 software has been used to find the significant process parameters to influence surface roughness. Work speed is the predominant parameter which affects the surface roughness found from S/N ratio and traverses feed followed by the depth of cut.

Kumar et al. (2015) have utilized L₉ Taguchi orthogonal array to find out the optimized cylindrical grinding process variables for C40E steel used extensively to manufacture shafts, axle, spindle, and studs. The process variable which affects the cylindrical grinding, such as work speed, table speed, depth of cut, and number of passes, wheel type and hardness of the material. However, work speed, depth of cut and Infeed have been selected for this study as process variables to get higher surface finish.

1.13 Experimental Setup

Work Material:

• Stainless steel having 38 mm diameter and 65 mm long. Experimental Factors and Levels:
• Process Variables – Work speed, Infeed, depth of cut
• Samples - 9 samples
• Levels - 3 levels for each variable
• Orthogonal Array – L₉ orthogonal array

External cylindrical grinding variables:

• Work Speeds - 91 rpm, 147 rpm, 210 rpm.
• Infeed – 0.06 mm/rev., 0.11 mm/rev., 0.17 mm/rev.
• Depth of Cut – 2 μm, 4 μm, 6 μm

1.13.1 Inference

The MINITAB 17 software with ANOVA has been used to examine the surface roughness value derived from the design of experiments. S/N ratio with smaller is the better criterion used to find the optimal process variables for minimum surface roughness value to get the higher surface finish. The percentage contribution in the descending order of process variables is work speed 44.95%, Infeed 34.78% and depth of cut 17.71% to get 0.238 μm surface roughness value. The optimized process variables to achieve 0.238 μm surface roughness value are 210 rpm of work speed, 0.11 mm/rev of infeed and 4 μm of the depth of cut. Finally, based on the above-optimized process variables, the confirmation test has been conducted. The difference between experimental and predicted result was only 3%.

Pal et al. (2012) have conducted an experimental study using universal tool and cutter grinding machine based on Taguchi L₉ orthogonal array for the EN24, EN 31 and Die steel  having different hardness value achieved through heat treatment process to find out the optimal process variables to get the higher surface finish. Since grinding is the final costlier manufacturing process, manufacturing of the component with proper selection of process variables fetches lesser manufacturing cost without compromising the product quality in terms of surface finish.

1.14 Experimental Setup

Work Material:

• Hardened and tempered EN24, EN 31 and Die steel having 30 mm diameter and 75 mm long.

Experimental Factors and Levels:

• Process Variables – Work speed, Hardness of material, Wheel grade.
• Samples - 9 samples
• Levels - 3 levels for each variable
• Orthogonal Array – L₉ orthogonal array

External Cylindrical Grinding Variables:

• Work Speeds - 100 rpm, 160 rpm, 200 rpm.
• Hardness – 50 HRc, 55 HRc, 60 Hrc.
• Grinding Wheel – Aluminium oxide with G46, G60, G80 grades.
• Wheel Speed – Constant 2850 rpm.

1.14.1 Inference

Regression modelling and Matlab Software R2011b has been used to find the optimized process variables such as work speed and hardness of the material with which grade of the grinding wheel. The result showed that the optimized process variables for the 1.07 μm surface roughness value are Hardness 55 HRc, Work speed 200 rpm with G60 grade of the grinding wheel.

From the literature survey it is evident that the improved productivity, accuracy, and the decreased cost of the grinding process depend on the correct choice of process parameters. Various methods in the manufacturing sector have been used for the optimization of machining process variables according to the scenario at that time (Almeida et al., 2006; Jayal et al., 2010; Yan & Li, 2013; He et al., 2015). The simple, systematic and robust optimization method called Taguchi is being used by researchers in this present scenario which offers the optimized process variables for quality of the product with lesser manufacturing cost and time by conducting a lesser number of experiments (Tsui, 1992; Montgomery, 1999; Shaji & Radhakrishnan, 2003; Lin et al., 2012). The design of experiments is framed by the orthogonal array and the variables are optimized by signal to noise ratio, finally, those are investigated by Analysis of Variance.

• Most of the researchers have investigated the influence of a limited number of process parameters on cylindrical grinding process which leads to incorrect conclusion.
• The simple, systematic, and robust optimization method called Taguchi is being used by researchers in this present scenario which offers the optimized process variables for quality of the product with lesser manufacturing cost and time by conducting a lesser number of experiments.
• Very few researchers worked so far on medium carbon steel alloys, which have so many applications like spring, gear production, bridges, and machine tools.

The intent of this article is to find the effect of various process parameters on various grinding operation on different steel alloy materials and the optimization techniques used. The following observations were identified;

• Only few researchers took coolant as input parameter and coolant flow rate as a parameter which might be a significant factor when considered for the experimentation.
• Experimental study could be carried out to study the effect of machining parameters on grinding temperature.
• It is found that the to obtain a better surface finish with extended material removal rate can only be achieved through proper selection of process variables, in turn, it yields poor surface finish and reduces the rate of production when selecting the improper process variables.

Nomenclature

OA - Orthogonal Array

MRR - Material Removal Rate

S/N - Signal to Noise Ratio

ANOVA - Analysis of Variance

mm – millimeter

cm – centimeter

m/min - metre per minute

L₉, L₁₈ - Latin square

mm³/s - cubic millimetre per second

N – newton

HRC - Rockwell Hardness C Scale

mm/rev - Millimetre per revolution

rpm - Revolution per minute

g/s - gram per second

^o C - degree Celcius

l/min - litre per minute

µm – micrometer

N/mm² - newton per millimetre square

Ra - Surface Roughness

SiC - Silicon Carbide

AISI - American Institute of Iron and Steel

BHN - Brinell Hardness Number

CNC - Computer Numerical Control