________________________________________________________________________________________________
Optimization the effect of input parameters on surface roughness and material removal rate of cylindrical grinding of AISI-1020 (low
carbon steel).
1Gurpal Singh, 2Sukhjinder Singh, 3Khushdeep Goyal
1,2,3
Department of Mechanical Engineering, Punjabi University, Patiala Abstract : In this present work, AISI-1020 steel is used to
find the effect of input parameters viz. wheel speed, work speed, coolant flow rate, abrasive material has been found on the surface roughness of cylindrical grinding. There are two levels of wheel speed and the three levels of each variable have been selected except wheel speed. The work piece material has been taken as heat treated AISI-1020.
To find the optimum set of parameters that maximize material removal rate and minimize surface roughness for AISI-1020. The experiment was designed by Taguchi’s analysis. Analysis of variance has been analyzed to find the effect of all input parameters on the outer response.
Keywords: input parameters, cylindrical grinding, surface roughness, material removal rate, ANOVA, Taguchi.
I. INTRODUCTION
Grinding was an abrasive machining process which was used as the finishing process to improve surface quality and a grinding wheel as the cutting tool [1]. It was used as the finishing process and grinding machine was used to shape the outside of an object. Surface roughness and material removal rate were the important response factors in cylindrical grinding [3]. In present study AISI- 1020 low carbon steel has been used as the work-piece material.
II. LITERATURE REVIEW
Pai et al. [1] investigated grinding machine has been of machining parameter (such as hardness of specimen, flow rate of coolant and depth of cut) on surface roughness in grinding of 6061AL –SIC25p specimen was investigated. Variation of surface roughness with machining parameters was mathematically modeled using response surface methodology. Kumar et al. [2]
studied that cylindrical grinding was one of the manufacturing cutting processes generally used in finishing operation. There were two attribute characteristics Surface finish and Surface roundness. In process parameters was taken wheel speed, work speed and depth of cut using Taguchi method to minimize noise ratio and surface roughness. Janardhan et al. [3]
studied that Surface grinding technique used in the manufacturing sector to generate smooth finish on flat surfaces. Surface quality and metal removal rate were the two important performance characteristics to be
considered in the grinding process. The economics of the machining process was affected by several factors such as abrasive wheel grade, wheel speed, depth of cut, table speed and material properties. Kadirgama et al.
[4] discussed optimization of cylindrical grinding when Carbon steel AISI (1042) and three effect variables (Work piece, diameter of work piece, Depth of cut) to minimize roughness with AL2O3 as a grinding wheel.
Ganesan et al. [5] discussed on 304 Stainless steel were conducted using Taguchi (design of experiment) of L9 orthogonal array was selected with 3 levels with 3 factors and output parameters of surface roughness was measured. The quality of the surface describes the relationship between the cutting speed, feed rate, depth of cut and surface roughness were measured. Quality of the surface depends on the size and shape of the work piece that is used. Kiyaka et al. [6] studied that in external cylindrical grinding; applied Taguchi methodology determines parameter for Minimum Surface Roughness and Vibration. Firstly an experiment conducts in Cylindrical Grinding Machine. L27 Orthogonal Array including three input parameters feed rate, Depth of cut, Work piece revolution and output irrational Surface roughness was applied to Design of experiment (D.O.E). George et al. [7] discussed the effect of machining variable depth of cut, work speed, work piece hardness using L9 orthogonal array was to measure surface roughness using MITUTOYO surf test sj-400 surface roughness tester. The developed model can be used by the different manufacturing firm to select right combination of machining parameters to achieve optimum surface roughness. Singh et al. [8] performed a experiment on AISI-4140 steel to find the effect of input parameters no. of passes, concentration of cutting fluid that has been found on the surface roughness. The main response parameters were Material removal rate and surface roughness. Taguchi’s L18 orthogonal array was selected with three levels of each variable and two levels of wheel speed .Analysis of variance (ANOVA) has been analyzed to see the effect of all the input parameters on the output response. Yadav et al. [9]
invested the effects of process parameters such as depth of cut, coolant flow rate and coolant nozzle angle in SAE- 8620 steel material and showed that Response
surface method was a powerful tool in design of experiments was used for optimization process. In this study the effect of process parameters such as depth of cut, coolant flow rate and coolant nozzle were taken as variables. The aim of their study to optimize process parameters to achieve surface quality and high material removal rate. Thakor et al. [10] studied the effect of input parameters (cutting fluids, work piece speed and depth of cut) on surface roughness and material removal rate on en8 steel. The experimental results find out set of parameters that minimize the surface roughness and maximize material rate with full factorial method and regression analysis with the conformation test .Surface roughness tester Mitutoyo SJ-210 was used for measuring surface roughness. The input parameters considered were: cutting fluids, work piece speed and depth of cut. Conformation test result showed that the various coolants reduces surface roughness of 0.4246 mm and increases material removal rate of 0.0974 gm/sec. Das et al. [11] presented a study to evaluate the performance of multilayer coated carbide inserts during dry turning of hardened AISI-4340 steel (47 HRC). In this study the experiments were planned based on Taguchi’s L27 Orthogonal array design and the effect of machining parameters (depth of cut, feed and cutting speed) on surface roughness was investigated by applying ANOVA. Results shown that cutting speed, feed and depth of cut exhibits negligible influence on surface roughness. A second order regression model was developed to find out the relationship between the machining parameters and surface roughness. Pawan et al. [12] studied the effects of abrasive tools on EN-24 steel by using three parameters wheel speed, table speed and depth of cut. Surface quality and metal removal rate were the most important performance characteristics in grinding process. In their study empirical models were developed by considered input parameters as control factors using response methodology. In this Response
surface methodology (RSM) was applied to determine the optimum machining parameters leading to minimum surface Roughness and maximum metal removal rate in Surface grinding process. Pal et al. [13] Studied grinding process, a machining parameter, such as hardness of the specimen, flow rate of the coolant and depth of cut while machining were chosen for evaluation by the response surface methodology. By response surface methodology, a complete realization of the process parameters and their effects were achieved.
III. OBJECTIVES
1. To work out optimum set of input parameters for minimum surface roughness and maximum material removal rate by using Taguchi and ANOVAs method.
2. To conduct the set of experiments on cylindrical grinding machine.
3. Optimize combination of input parameters wheel speed, work speed, depth of cut, no. of passes to achieve minimum surface roughness.
IV. DESIGN OF EXPERIMENT
In this study AISI-1020 is a low harden-ability and low tensile carbon steel with Brinell hardness 119-235 and tensile strength of 410- 790 mpa. AISI-1020 has wide application in industrial sector, when require high mach inability, high strength, high ductility and good weld ability, also application in component axles, machinery parts, shafts, and spindles.
3.1 Work-piece Material
In our experiment AISI-1020 steel is used as a work- piece material, which is a low carbon steel
Chemical composition of AISI-1020.
Table no- 1: chemical composition of AISI-1020.
CARBON SILICON MANGANESE PHOSPHORUS SULPHUR
0.230% 0.121% 0.363% 0.040% 0.028%
4.2 Experimental procedure
The experiment was performed by step by step Sequence of operations, to develop model for surface roughness and optimize material removal rate. The outermost layer (2mm) was turned of during using turning process.
Hardening process
After turning process heat treated the material for hardening process. The specimen were though hardened to obtain a uniform hardness of 55 N/mm2
Grinding process
An experiment was performed by HMT 130 k grinding machine. There were two factors i.e. surface roughness and material removal rate have been selected as response factors. Out of various factors that influence response factors, the most critical response factors such
as wheel speed, work piece speed, abrasive material, depth of cut, concentration of cutting fluid and number of passes have been selected as input parameters.
The list of various input parameters along with their levels used for performing experiment is shown in Table 2 and Table 3 shows the L18 orthogonal array along with independent variables and their selected levels used for experiment.
Table 2 Different input parameters and their levels Input parameters Level1 Levels2 Level3 Grinding wheel 2200 2400 -
Work speed 112 224 315
Abrasive material 60w 60g 60b
Depth of cut 15 25 35
Fluid flow rate 3.13 7.53 11.13
No .of passes 3 4 5
Table no 3 Taguchi L-18 Orthogonal Array Experiment Design
A B C D E F
EXP. NO. Wheel speed (rpm)
Work piece speed (rpm)
Abrasive material
Depth of cut Cutting flow rate
No. of passes
1 2200 112 60w 15 3.13 3
2 2200 112 66g 25 7.53 4
3 2200 112 60b 35 11.13 5
4 2200 224 60w 15 7.53 4
5 2200 224 60g 25 11.13 5
6 2200 224 60b 35 3.13 3
7 2200 315 60w 25 3.13 5
8 2200 315 60g 35 7.53 3
9 2200 315 60b 15 11.13 4
10 2400 112 60w 35 11.13 4
11 2400 112 60g 15 3.13 5
12 2400 112 60b 25 7.53 3
13 2400 224 60w 25 11.13 3
14 2400 224 60g 35 3.13 4
15 2400 224 60b 15 7.53 5
16 2400 315 60w 35 7.53 5
17 2400 315 60g 15 11.13 3
18 2400 315 60b 25 3.13 4
V. RESULTS AND DISCUSSION 3.2 Result for surface roughness
The results for all the 18 experiments according to Taguchi’s L-18 orthogonal array with different input factor levels for Surface Roughness are shown in Table 4, the calculated value of S/N ratio was shown in the last column and it has been found with lower the better approach.
It is observed that low surface roughness obtained was 0.38µm which was wheel speed of 2200 rpm, work speed 224 rpm, 60 green abrasive material, depth of cut 25 µm and flow rate 11.13 l/min.
5.2 Result for material removal rate
It is observed that maximum removal rate obtained was 0.38µm which was wheel speed of 2200 rpm, work
speed 315 rpm, 60 green abrasive material, depth of cut 35 µm, flow rate 7.53 l/min and number of passes 3.
5.3 Experiment result
1. Surface Roughness Tester was used to find the surface roughness for various parameters.
2. Material Removal rate ( MRR ) was calculated by
3. MRR= (weight before Machining-weight after Machining)/ Machining time
4. It was measured by theoretical equation.
By using ANOVA, results obtained from the experiments were analyzed
Table no 4. Results for surface roughness (RA) and material removal rate
A B C D E F
EXP.
NO.
Wheel speed (rpm)
Work piece speed (rpm)
Abrasive material
Depth of cut
Cutting fluid rate
No. of passes
RA S/N
Ratio
MRR S/N ratio
1 2200 112 60w 15 3.13 3 0.54 5.35212 0.41 -7.7443
2 2200 112 66g 25 7.53 4 0.49 6.19608 1.52 3.6369
3 2200 112 60b 35 11.13 5 0.60 4.43697 1.92 5.6660
4 2200 224 60w 15 7.53 4 0.58 4.73144 0.61 -4.2934
5 2200 224 60g 25 11.13 5 0.38 8.40433 1.85 5.3434
6 2200 224 60b 35 3.13 3 0.79 2.04474 0.94 -0.5374
7 2200 315 60w 25 3.13 5 0.55 5.19275 0.49 -6.1961
8 2200 315 60g 35 7.53 3 0.46 6.74484 3.82 11.6413
9 2200 315 60b 15 11.13 4 0.63 4.01731 0.55 -5.1927
10 2400 112 60w 35 11.13 4 0.49 6.19608 0.30 -10.4576
11 2400 112 60g 15 3.13 5 0.44 7.13095 1.38 -8.4043
12 2400 112 60b 25 7.53 3 0.79 2.04746 1.19 1.5109
13 2400 224 60w 25 11.13 3 0.57 4.88250 1.93 5.7111
14 2400 224 60g 35 3.13 4 0.35 9.11854 2.47 7.8639
15 2400 224 60b 15 7.53 5 0.79 2.04746 0.73 -2.7335
16 2400 315 60w 35 7.53 5 0.50 6.02006 0.72 -2.8534
17 2400 315 60g 15 11.13 3 0.59 4.58296 2,40 7.6042
18 2400 315 60b 25 3.13 4 0.72 2.8633 1.48 3.4052
5.4 Analysis of variance for surface roughness From below figure it was clearly show that F0.05 =5.99, F(2,6)0.05=5.14.The abrasive material was most significant parameters for surface roughness. The error variation of 10.33 is due to uncontrollable parameters.
The number of passes, grain size, wheel speed and concentration of cutting fluid are insignificant parameters and they have lowest percentage contribution then abrasive material. Response values for S/N ratios for surface roughness are given in table 5.
Table no. 5. Analysis of Variance for S/N ratio for Surface Roughness (Ra) Source of
variation
Sum of
squares
Degree of freedom
Mean square F-value Status Percentage contribution Wheel
speed(rpm)
0.2786 1 0.2786 0.22 insignificant 0.385
Work speed(rpm)
0.3974 2 0.1987 0.16 insignificant 0.545
Abrasive material
51.7025 2 25.813 20.77 Significant 70.91
Depth of cut(µm)
4.0451 2 2.0225 1.63 insignificant 5.60
Concentration of cutting fluid(l/min)
2.1276 2 1.0638 0.85 insignificant 2.94
Number of passes
6.2739 2 3.1370 2.52 insignificant 8.68
Residual error 7.4662 6 1.2444 10.33
Total 72.2912 17
Table no. 6. Response table for mean signal-to- noise ratio for surface roughness
Level A B C D E F
Wheel speed Work speed Abrasive material Depth of cut Concentration of cutting fluid
Number of passes
1. 5.235* 5.227* 5.396 4.463 5.283 4.276
2. 4.987 5.205 7.030* 4.929 4.631 5.518
3. - 4.901 2.908 5.761* 5.419* 5.539*
Delta 0.249 0.325 4.122 1.118 0.788 1.263a
Rank 6 5 1 3 4 2
*indicator higher S/N ratio
to achieve minimum surface roughness the optimum set of parameters are grinding wheel speed of 2200 rpm;
work-piece speed at 112 rpm; abrasive material of 60 green wheel; depth of cut is 15 mm; concentration of cutting fluid is 11.13 and number of passes are equal to 5
5.5 Analysis of variance for material removal rate(gm/min)1
From below figure it is clearly show that F0.05 =5.99, F(2,6)0.05=5.14.The abrasive material is most significant parameters for surface roughness. The error variation of 10.33 is due to uncontrollable parameters.
The number of passes, grain size, wheel speed and concentration of cutting fluid are insignificant parameters and they have lowest percentage contribution then abrasive material.
Response values for S/N ratios for surface roughness are given in table.
Source of Sum of Degree of Mean Square F-Value Status Percentage
Variance Squares Freedom Contribution
Wheel Speed 0.0254 1 0.0254 1.2337 insignificant 3.7518
Work Speed 74.8363 2 37.41815 1.8174 insignificant 11.05
Abrasive Material
238.8492 2 119.4246 5.8005 significant 35.28
Degree of Freedom
123.2024 2 61.6012 2.9920 insignificant 18.19
No. of Passes 73.5800 2 36.79 1.7869 insignificant 10.86
Concentration of Cutting Fluid
42.9730 2 21.4865 1.0436 insignificant 6.34
Residual Error 123.53.13 6 20.58855 18.24
Total 676.9976 17
Table no. 7. Response table for S/N ratio for MRR Level Wheel Speed
(rpm)
work speed (rpm)
grain size (mesh/inch)
Depth of cut(µm)
flow rate (l/min)
No. of passes 1 0.2582* -2.6321 -4.3056 -3.4607 -1.9372 3.0310*
2 0.1819 1.8907* 4.6126* 2.2353* 1.1515 -0.8413
3 - 1.4014 0.3531 1.8855 1.4458* -1.5296
Delta 0.0763 4.5228 8.9182 5.6959 3.3829 4.5606
Rank 6 4 1 2 5 3
Result and Discussion
3.3 Main effect plots for surface roughness The optimum set of parameters for maximize MRR are;
grinding wheel speed of 2200 rpm; work-piece speed 224 rpm; abrasive material of 60 green wheel; depth of cut is 25 mm; concentration of cutting fluid is 7.13 and number of passes are equal to 3.
In this experimental work, the effect of independent variables for the surface roughness and material removal rate using ANOVA method with conclusions are:
Main effect plot for surface roughness main effects plot of S/N ratio for surface roughness
1. S/N value is decreasing slightly when we change grinding wheel speed is increased from2200rpm to 2400 rpm. It is found that as we increase grinding wheel speed the surface finishing is decreases.
2. S/N value is decreasing when work piece speed is increasing from 112 rpm to 224 rpm and it will further decreasing S/N ratio at wheel speed 315.
3. S/N value is increasing when green wheel as compared to white wheel and S/N value is decreasing by
using black wheel. The S/N value is higher at green wheel. It is her optimum level of abrasive material.
4. When depth of cut was increased from 15 mm to 25 mm the S/N ratio is increased and further increased to 35 mm. The S/N value is higher at depth of cut 35 mm, so it is optimum level of depth of cut.
5. S/N value in flow rate will decrease when goes to 3.13 to 7.53 and it will increase, While goes at 11.13.
The higher mean S/N value corresponds at 11.33.
6. The S/N value is increased as number of passes changes is increased from 3 to 4 and they will further slightly increase as they are increased from 4 to 5 passes.
5.2 Main effect plot for S/N ratio for MRR
1. The S/N ratio is constant at both wheel speed changed from 2200 rpm to 2400 rpm.
2. The S/N ratio increases as speed is increased from 112 rpm to 224 rpm and S/N ratio is decreased when speed is increased from 224 rpm to 315 rpm.
3. In case of abrasive material, the S/N ratio is maximum at green wheel as compared to white wheel or black wheel.
4.The S/N ratio increases as we increase depth of cut from 15 mm to 25 mm and S/N ratios decreased when we increase depth of cut from 25 mm to 35 mm. the highest ratio is at depth of cut 25 mm , so it is the optimum level for better roundness.
5.The S/N ratio increases when concentration of cutting fluid is changed from 3.13 to 7.13 and they will further slightly increases when concentration of cutting fluid changes from 7.13 to 11.13.the S/N ratio is highest at concentration of cutting fluid at 7.13 hence this is her optimum level for minimum roundness error.
6. The S/N ratio decreases when number of passes increased from 3 to 4, and they will further decrease when S/N ratio is increased from 4 to 5.
VI. CONCLUSIONS
In this experiment work, the result of six independent variables viz. grinding wheel, abrasive material, work piece speed, concentration of cutting fluid, depth of cut and number of passes have been studied for material removal rate using taguchi and anova method. The important conclusions are as follows:
It has been found that abrasive material is significant parameters.
Abrasive material is found to be most significant parameters for surface roughness.
Wheel speed has negligible effect on surface roughness.
Experiment work found that out of various parameters abrasive material is the most significant parameter.
The wheel speed is found to be least significant parameter.
Abrasive material, depth of cut and number of passes has higher percentage contribution then wheel speed, concentration of cutting fluid and work piece speed.
The optimum set of parameters for minimum out-of-roundness are; grinding wheel speed of 2200 rpm; work-piece speed 224 rpm; abrasive material of 60 green wheel; depth of cut is 25 mm; concentration of cutting fluid is 7.13 and number of passes are equal to 3.
4. Optimum set of parameters for surface roughness and material removal rate
In this experiment work, the result of six independent variables viz. grinding wheel, abrasive material, work piece speed, concentration of cutting fluid, depth of cut and number of passes have been studied for material removal rate using taguchi and anova method.
It is found that to achieve minimum surface roughness the optimum set of parameters are grinding wheel speed of 2200 rpm; work-piece speed at 112 rpm; abrasive material of 60 green wheel; depth of cut is 15 mm;
concentration of cutting fluid is 11.13 and number of passes are equal to 5.
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