 Open Access
 Total Downloads : 305
 Authors : R. N. Patil, B.B Ahuja
 Paper ID : IJERTV2IS100925
 Volume & Issue : Volume 02, Issue 10 (October 2013)
 Published (First Online): 25102013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Analysis of Boric Acid as A Lubricant In Surface Grinding Process Based on the Taguchi Method
R. N. Patil #1, B.B Ahuja 2
Research Scholar, Deputy Director, Bharati Vidyapeeth University, Govt. College of Engineering,
Pune, India. Pune, India.
Abstract
Grinding is one of the most popularly used surface finishing process characterized by excellent process capabilities in terms of surface finish and material removal rate. It has wide applications in manufacturing industry as it can handle a wide variety of workpiece materials and forms. However it needs very high specific energy and gives rise to very high temperature, which results into high residual stresses, micro cracks and burn. To reduce these ill effects normally coolants are employed, which reduce the friction and provides effective cooling of work wheel interface. Conventional applications of coolants are many times ineffective due to inaccessibility of the fluid to grinding zone and film boiling. Many of the conventional coolants pose health hazard and support the green house effect. This paper deals with the use of solid lubricant, boric acid carried in the proper medium to grinding zone and reducing the friction and hence eliminating the thermal ill effects on the grinding process. Results show that the grinding force, grinding energy and the surface roughness decreases with the help of the use of boric acid compared with dry grinding and grinding with the conventional coolants.
Keywords Coolant, Boric acid, Grinding, Lubricant, Taguchi method.

Introduction
Grinding is the process of material removal in the form of small chips by mechanical action of abrasive particles bonded together in grinding wheel. It is basically a finishing process employed for producing close dimensional and geometrically accuracies and smooth surface finish. However in some applications, the grinding process is applied for higher material removal rates and is also referred as abrasive
machining[1]. It is distinguished from many other machining processes on aspects like tool geometry, large contact area and high friction between tool and workpiece and high specific energy requirement. The high specific energy consumed during the grinding process results into heat. Major portion of heat is transferred to workpiece[2]. The severe heat input affects the workpiece quality. It may induce thermal damage such as burn, surface and subsurface cracks, metallurgical phase transformations, residual tensile stresses and dimensional distortions and many more ill effects[35]. To control the ill effects of heat generated during the grinding process at the cutting zone and ensure the quality of the workpiece, coolants are employed. Grinding fluids are introduced into grinding operations to reduce and transfer heat during grinding, lubricate during chip formation, wash the grinding wheel of loose chip and swarf, chemically aid the grinding action or machine maintenance. Grinding fluids of five different categories are used popularly. These are petroleum base and mineral base cutting oils, water soluble oils, synthetic fluids, semi synthetic fluids, water plus additives, including rust inhibitors, water treatments (for examples, softeners), and defoaming agents.
Several researchers have carried out research on the coolant type, its composition and supply characteristics[6]. However, during grinding operation coolants are relatively inaccessible to the grinding arc. Therefore, often it is ineffective in reducing the grinding zone temperature, but only provides bulk cooling of the work piece. The stiff boundary layer forming around the rotating grinding wheel also inhibits the access of the fluid to the grinding zone[7, 8]. Conventional coolants lose their cooling properties upon film boiling and that the film boiling temperature of these coolants are lower which affects the
effectiveness of grinding fluid[9]. However, flooded coolant operation allows effective removal of grinding swarf, which is needed in grinding. Conventional grinding fluids are a source of environmental hazards and increasingly strict regulations are coming up with regard to their use and disposal[10]. The cost of these fluids is also high. As an alternate to conventional fluids several other coolants/ lubricants are tried. Some efforts have been carried out with the help of solid lubricants as a substitute which has shown substantial improvement in the process[11]. Further these factors prompted investigations on the use of biodegradable coolants, minimum quantity coolant and coolant free grinding[12]. But any attempt to minimize or avoid the coolant can be dealt with only by the substitution of the functions normally met by the coolants by some other means. Grinding employing cryogenic cooling by liquid nitrogen and minimum Quantity Lubrication (MQL) by ester oil supplied in the form of spot jet or spray mist are the alternative approaches towards this end. The effect of graphite nanoplatelets in solid lubrication grinding are also studied which gives effective way of improving the process[13]. The results are very encouraging but the required amount of nanomaterial significantly increases the overall cost.
The objective of the present investigation is to employ boric acid, which is a high temperature solid lubricant, as a means to reduce the heat generated at the grinding zone and study the process performance. An experimental setup is designed and built for comparative performance analysis of boric acid assisted grinding with dry and conventional flooded coolant grinding.

Experimental Setup
An experimental set up is designed and fabricated to ensure proper delivery of the solid lubricant with the carrier to the grinding zone. This is devised with the help of dead weight loaded cylinder. The mix of boric acid and the carrier oil is introduced into the cylinder. Constant pressure is maintained on this mix with help of a piston which is in turn loaded by the dead weight mounted on the piston rod as shown in Fig. 1. The flow of mix is controlled with the help of the flow control valve. The mix is impinged on the grinding zone through a nozzle with an opening of 1mm * 25 mm. The width of the opening corresponds to the width of grinding wheel. The mix is first delivered to the soft rubber wheel, which is free to rotate and is maintained in constant contact with the grinding wheel. The paste is eventually transferred to the grinding wheel and then
to the grinding zone to serve as a lubricant in the grinding zone.
Fig. 1. Schematic diagram of Experimental set up.

Primary Experimentation
The grinding operation is carried out on horizontal reciprocating table surface grinding machine. The test specimen of size 100 mm* 50 mm *
10 mm thickness is used for the experimentation. Grinding is carried out along the length of the workpiece.
The ultimate effect in which industry is primarily interested is the quality of surface finish obtained through the process and the amount of energy consumed for the process. Hence for primary investigations following factors are kept constant. The details of which are provided in Table 1.
Primary experimentation is carried out on alloy steel AISI 4140 to observe the effect of uses of solid lubricants on the process. The results of experimentation are tabulated in Table 2.
Table 1. Factors kept constant in grinding.
Factor
Details
Workpiece material
Alloy steel AISI 4140
Grinding machine
Horizontal sindle surface grinding, machine, PMT make, Model SG 42, 5.5 kw
Grinding Wheel
A60L5V10, Diameter 300 mm, width 25 mm.
Wheel velocity
30m/min
Table feed rate
10m/min
Dressing conditions
With single point diamond dresser, 1 carat, in dry condition.
Table 2. Observations for preliminary experimentation.
YData
YData
Dept h of Cut,
Âµ
Dry Grinding
Wet Grinding
Boric Acid
Force, N
Ra,
Âµ
Force, N
Ra,
Âµ
Force, N
Ra,
Âµ
10
434.42
0.46
403.16
0.46
200.02
0.41
20
506.82
0.47
470.35
0.47
233.35
0.41
30
579.21
0.47
537.54
0.47
266.69
0.42
40
651.62
0.47
604.73
0.47
300.03
0.42
50
724.02
0.47
671.93
0.47
333.36
0.42
Dept h of Cut,
Âµ
Dry Grinding
Wet Grinding
Boric Acid
Force, N
Ra,
Âµ
Force, N
Ra,
Âµ
Force, N
Ra,
Âµ
10
434.42
0.46
403.16
0.46
200.02
0.41
20
506.82
0.47
470.35
0.47
233.35
0.41
30
579.21
0.47
537.54
0.47
266.69
0.42
40
651.62
0.47
604.73
0.47
300.03
0.42
50
724.02
0.47
671.93
0.47
333.36
0.42
To analyze the forces under various cutting condition the graph of force versus the depth of cut is plotted. It indicates that the force increases with increasing depth of cut, but the magnitude depends upon the coolant used for the operation. It is observed that using the solid lubricants the force requirement is reduced quality of surface finish is improved.
Scatterplot of Dry, Wet, Boric Acid vs Depth of cut
Scatterplot of Dry, Wet, Boric Acid vs Depth of cut
700
600
500
400
300
200
Variable
Dry
Wet
Boric Acid
700
600
500
400
300
200
Variable
Dry
Wet
Boric Acid
10
20
30
Depth of cut
40
50
10
20
30
Depth of cut
40
50
YData
YData
Fig. 2. Effect of coolants on force under varying depths of cut
Scatterplot of Dry, Wet, Boric Acid vs Depth of cut
Scatterplot of Dry, Wet, Boric Acid vs Depth of cut
700
600
500
400
300
200
Variable Dry
Wet
Boric Acid
700
600
500
400
300
200
Variable Dry
Wet
Boric Acid
10
20
30
Depth of cut
40
50
10
20
30
Depth of cut
40
50
Fig. 3. Surface roughness for AISI 4140 using different coolants.

Taguchi methods for design of experiments
Taguchi methods of experimental design provide a simple, efficient and systematic approach for the optimization of experimental designs for performance quality and cost. It has been proved successful to many manufacturing situations. The traditional experimental design procedures focus on the average product or process performance characteristics. But the Taguchi method concentrates on the effect of variation on the product or process quality characteristics rather than on its averages. That is, the Taguchis approach makes the product or process performance insensitive (robust) to variation to uncontrolled or noise factors. Taguchi recommends that this can be done by the proper design of parameters during the parameter design phase of offline quality control. He designed certain standard orthogonal arrays (OAs) by which simultaneous and independent evaluation of two or more parameters for their ability to affect the variability of a particular product or process characteristic can be done in a minimum number of tests. Subsequently, decision is made for the optimum combination of these parameters.
The following objectives are fixed for the study:

Find out the effect of use of boric acid on force required for grinding and surface roughness obtained, and

Conduct a comparative study of the applications to various alloy steels.
The surface roughness (Ra) of the ground piece and the force required (F) are taken as the quality characteristics of the process. The surface finish is a direct process result and is measured by a Mitutoyo surftest. For force measurements a dynamometer is used, which recorded the two components of force. The resultant force is worked out and is considered as a single response.
The operating conditions such as speed and feed are held constant during the experimentation. However, infeed rate, workpiece material and flow rate of the lubricant is considered for variation. Three levels, having equal spacing, within the operating range are selected for each of the factors as shown in Table 3. By selecting three levels the curvature or the nonlinearity effects could be studied.
Table 3. Levels in Experimentation
Factor
Level 1
Level 2
Level 3
Material
AISI 4140
AISI 4310
AISI 52100
Infeed (Depth of cut), (Âµm)
10
30
50
Flow Rate, (mm3/min)
20
40
60
Table 4 shows the experimental layout of an L27 0rthogonal array consisting of three control factors and three levels according to Taguchis suggestion. The numbers in each column indicate the levels for the specific factors and from the L27 orthogonal array 27 experiments are required to examine the total manufacturing parameters. The L27 orthogonal Array which meets this requirement is selected.
Trial
Material
In feed ( Âµm)
Flow Rate (mm3/min)
1
1
1
1
2
1
1
1
3
1
1
1
4
1
2
2
5
1
2
2
6
1
2
2
7
1
3
3
8
1
3
3
9
1
3
3
10
2
1
2
11
2
1
2
12
2
1
2
13
2
2
3
14
2
2
3
15
2
2
3
16
2
3
1
17
2
3
1
18
2
3
1
19
3
1
3
20
3
1
3
21
3
1
3
22
3
2
1
23
3
2
1
24
3
2
1
25
3
3
2
26
3
3
2
27
3
3
2
Trial
Material
In feed ( Âµm)
Flow Rate (mm3/min)
1
1
1
1
2
1
1
1
3
1
1
1
4
1
2
2
5
1
2
2
6
1
2
2
7
1
3
3
8
1
3
3
9
1
3
3
10
2
1
2
11
2
1
2
12
2
1
2
13
2
2
3
14
2
2
3
15
2
2
3
16
2
3
1
17
2
3
1
18
2
3
1
19
3
1
3
20
3
1
3
21
3
1
3
22
3
2
1
23
3
2
1
24
3
2
1
25
3
3
2
26
3
3
2
27
3
3
2
Table 4. L27 Orthogonal array.


Experimentation
By choosing the L27 orthogonal array the issue of repeating the tests is sorted out, as the array itself recommends the repetitions. This helps to obtain more reliable information from the statistical analysis of the experiment. Quality characteristics, i.e. surface roughness (Ra) in Âµm and force (F) in N are noted by the measurement systems. For having consistent results with Ra, pieces are ground giving seven sets of upward and downward spark out passes. The sample data obtained for the quality characteristics, i.e. surface roughness (Ra in Âµm) and force in N in the boric acid assisted grinding is shown in Table 5. It is observed that there is considerable consistency in the obtained data and it is reliable for statistical analysis.
Table 5. Sample data for force and surface roughness in boric acid assisted grinding.
Trial
Force (N)
Surface roughness (Âµm)
1
269.14
0.42
2
277.46
0.43
3
281.69
0.45
4
305.03
0.42
5
309.68
0.43
6
319.26
0.45
7
305.03
0.42
8
314.46
0.43
9
319.25
0.45
10
274.09
0.46
11
278.27
0.47
12
286.87
0.50
13
326.99
0.47
14
337.10
0.48
15
342.24
0.51
16
601.08
0.49
17
610.24
0.50
18
629.11
0.53
19
288.10
0.47
20
297.01
0.48
21
301.53
0.51
22
564.91
0.49
23
573.52
0.50
24
591.25
0.53
25
536.67
0.49
26
553.26
0.50
27
561.69
0.53
20
*
*
*
21
*
*
*
22
55.218
13.4306
576.560
23
*
*
*
24
*
*
*
25
54.817
12.7298
550.540
26
*
*
*
27
*
*
*
20
*
*
*
21
*
*
*
22
55.218
13.4306
576.560
23
*
*
*
24
*
*
*
25
54.817
12.7298
550.540
26
*
*
*
27
*
*
*

Analysis of the data

Evaluation of S/N ratios

Taguchi suggests the transformation of the repetition data in a trial into a consolidated single value called the S/N ratio. Here, the term signal represents the desirable value (mean) and the noise represents the undesirable value (standard deviation). So the S/N ratio represents the amount of variation present in the quality characteristic. Depending upon the objective of quality characteristic there can be various types of S/N ratios. Here the desirable objectives, the force required for the grinding operation and the surface roughness should be lower. Hence the lowerthebetter type S/N ratio, as given below is applied for transforming the observed data:
From the calculated values of signal to noise ratio, the values are obtained for the various levels of the factors and tabulated in Table 7.
Level
Material
In feed (Âµm)
Flow rate
(mm3/min)
1
49.53
49.06
53.27
2
51.74
51.87
51.21
3
53.15
53.49
49.95
Delta
2
1
3
Rank
2
1
3
Level
Material
In feed (Âµm)
Flow rate
(mm3/min)
1
49.53
49.06
53.27
2
51.74
51.87
51.21
3
53.15
53.49
49.95
Delta
2
1
3
Rank
2
1
3
Table 7 Signal to Noise Ratio: Smaller the better.
Z = log10 {sum (y**2)/n}
Where Z is the S/N ratio for the lowerthebetter case, y is the measured quality characteristic for the ith repetition, and n the number of repetitions in a trial. The S/N ratios, standard deviation and mean for grinding force for boric acid assisted grinding for various alloy steels, the varying flow rates and infeed rates are calculated from the observed data and tabulated in Table 6.
Trial
S/N Ratio
Standard Deviation
Mean
1
48.822
6.3851
276.097
2
*
*
*
3
*
*
*
4
49.865
7.2559
311.323
5
*
*
*
6
*
*
*
7
49.910
7.2351
312.913
8
*
*
*
9
*
*
*
10
48.936
6.5161
279.743
11
*
*
*
12
*
*
*
13
50.513
7.7588
335.443
14
*
*
*
15
*
*
*
16
55.757
14.292
613.477
17
*
*
*
18
*
*
*
19
49.414
6.8335
295.547
Trial
S/N Ratio
Standard Deviation
Mean
1
48.822
6.3851
276.097
2
*
*
*
3
*
*
*
4
49.865
7.2559
311.323
5
*
*
*
6
*
*
*
7
49.910
7.2351
312.913
8
*
*
*
9
*
*
*
10
48.936
6.5161
279.743
11
*
*
*
12
*
*
*
13
50.513
7.7588
335.443
14
*
*
*
15
*
*
*
16
55.757
14.292
613.477
17
*
*
*
18
*
*
*
19
49.414
6.8335
295.547
Table 6. Calculated S/N ratio, Standard deviation and Mean for the experimentation.
The main effect plot (the data means) for the calculated S/N ratio is calculated and presented graphically as shown in the Fig. 4. The highest S/N ratio always yields the optimum quality with minimum variance. Therefore, the level with a higher value determines the optimum level of each factor. For the experimentation carried out the best results would be obtained for AISI4140, the workpiece material, infeed rate of 10
Mean of SN ratios
Mean of SN ratios
Âµm, and flow rate of 60mm3/min.
Main Effects Plot (data means) for SN ratios
Material In feed, Âµm
49
50
51
52
53
Main Effects Plot (data means) for SN ratios
Material In feed, Âµm
49
50
51
52
53
1
2
3
1
2
3
Signaltonoise: Smaller is better
Signaltonoise: Smaller is better
1
1
2
Flow Rate mm3/min
2
Flow Rate mm3/min
3
3
1
1
2
2
3
3
49
50
51
52
53
49
50
51
52
53
Fig. 4. Main Effects Plot (data means) for SN rations (Signal to Noise: Smaller is better) for Boric Acid.
From the calculated values of means, the values are obtained for the various levels of the factors and
tabulated in Table 8 and the same is represented graphically in the Fig. 5.
Table 8 Response Table for Means
Level
Material
In feed, Âµm
Flow rate
mm3/min
1
300.1
283.8
488.7
2
409.6
407.8
380.5
3
474.2
492.3
314.6
Delta
174.1
208.5
174.1
Rank
2
1
500
450
400
350
300
Main Effects Plot (data means) for Means
Material In feed, Âµm
500
450
400
350
300
Main Effects Plot (data means) for Means
Material In feed, Âµm
1
2
3
1
2
3
1
1
2
Flow Rate mm3/min
2
Flow Rate mm3/min
3
3
1
1
2
2
3
3
500
450
400
350
300
500
450
400
350
300
Mean of Means
Mean of Means
Fig. 5. Main Effects Plot (data means) for Means for Boric Acid.
2.4.2 The regression Analysis
Taguchi technique can be used for the prediction of the full factorial design. The regression equation from full factorial prediction is
F = 337 + 0.154 s + 5.21 d – 4.35 f
Where, F is the grinding force in Newton, s is the shear stress of workpiece material in MPa, d is the infeed in micrometers and f is flow rate in mm3/min
Table8. Regression Table
Predictor
Coefficient
Standard Error Coefficient
T
P
Constant
337.44
32.63
10.34
0.000
s, MPa
0.15386
0.02509
6.13
0.000
d, Âµm
5.2129
0.5753
9.06
0.000
F,
mm3/min
4.3519
0.5753
7.57
0.000
S = 48.8125 RSq = 88.5% RSq(adj) = 87.0%
2.4.3. Analysis of Variance
ANOVA is a computational technique to estimate quantitatively the relative contribution which each controlled parameter makes on the overall measured response and is expressed as a percentage. Thus information about how significant the effect of each controlled parameter is on the experimental results can be obtained. The total variation in response is decomposed into variation due to various controlled factors and their interactions and due to the error involved in the experimentation. The ANOVA can be done with the raw data or with the S/N data. The ANOVA based on the raw data signifies the factors which affect the average response rather than reducing the variation. But ANOVA based on the S/N data takes into account both these aspects and so it is used here. Since it is a saturated design where all columns are assigned with the factors, the variations due to error are estimated by pooling the estimates of the factors having least variance. This also helped in determining the Fisher test (Ftest) factor for finding out the confidence level of the results. The ANOVA is done for all the quality characteristics under study.
Table 9. Analysis of Variance
Source
Degrees of Freedom
Sum of Squares
MS
F
Value
P
Value
Regression
3
421642
140547
58.99
0.000
Residual error
23
54801
2383
Total
26
476443
Source
Degrees of Freedom
Seq. Sum of Squares
s, MPa
1
89627
d, Âµm
1
195652
F, mm3/min
1
136362



Interpreted Graphs For Force As A Quality Characteristic:
On the basis of the interpreted results of force required for grinding operation with the help of boric acid as solid lubricant for the materials selected, AISI 4140, AISI 4340 and AISI 52100 are given in Fig.6 to Fig. 8.
AISI 4140, Boric Acid as lubricant
AISI 4140, Boric Acid as lubricant
500
Force, N 300
500
Force, N 300
55
55
100
20
25
Infeed, Âµm
100
20
25
Infeed, Âµm
40
60
10
40
60
10
40
40
Flow Rate, mm3/min
Flow Rate, mm3/min
Fig. 6. AISI 4140, Boric Acid as lubricant
AISI 4340, Boric Acid as Lubricant
AISI 4340, Boric Acid as Lubricant
600
Force, N 400
600
Force, N 400
55
55
200
20
25
Infeed, Âµm
200
20
25
Infeed, Âµm
10
Flow Rate, mm3/min
10
Flow Rate, mm3/min
40
40
40
40
60
60
Fig. 7. AISI 4340, Boric Acid as lubricant
AISI 52100, Boric Acid as Lubricant
AISI 52100, Boric Acid as Lubricant
600
Force, N 500
400
600
Force, N 500
400
300
55
300
55
20
40
60
10
20
40
60
10
40
25 Infeed, Âµm
40
25 Infeed, Âµm
Flow Rate, mm3/min
Flow Rate, mm3/min
Fig. 8. AISI 52100, Boric Acid as lubricant

Analysis for Surface Roughness
On the similar lines of the force, Taguchi analysis is done for surface roughness obtained in the boric acid assisted grinding process. The S/N ratios, standard deviation and mean for surface roughness for boric acid assisted grinding for various alloy steels, the varying flow rates and infeed rates are calculated from the observed data are tabulated in Table 10.
Table 10. Calculated S/ N ratio, Standard deviation and Mean..
Trial
Ra, Âµ
Analysis of Force
SNRA1
STDE1
MEAN1
1
0.42
7.25996
0.0152
0.433333
2
0.43
*
*
*
3
0.45
*
*
*
4
0.42
7.25996
0.0152
0.433333
5
0.43
*
*
*
6
0.45
*
*
*
7
0.42
7.25996
0.0152
0.433333
8
0.43
*
*
*
9
0.45
*
*
*
10
0.46
6.43019
0.0208
0.476667
11
0.47
*
*
*
12
0.50
*
*
*
13
0.47
6.25007
0.0208
0.486667
14
0.48
*
*
*
15
0.51
*
*
*
16
0.49
5.90067
0.0208
0.506667
17
0.50
*
*
*
18
0.53
*
*
*
19
0.47
6.25007
0.0208
0.486667
20
0.48
*
*
*
21
0.51
*
*
*
22
0.49
5.90067
0.0208
0.506667
23
0.50
*
*
*
24
0.53
*
*
*
25
0.49
5.90067
0.0208
0.506667
26
0.50
*
*
*
27
0.53
*
*
*
From the calculated values of signal to noise ratio, the values are obtained for the various levels of the factors and tabulated in Table 11.
Table 11.Signal to Noise Ratios Smaller is better
Level
Material
In feed, Âµm
Flow rate mm3/min
1
7.260
6.647
6.354
2
6.194
6.470
6.530
3
6.017
6.354
7.532
Delta
1.243
0.293
0.233
Rank
1
2
3
The main effect plot (the data means) for the calculated S/N ratio is calculated and presented graphically as shown in the Fig. 9. The highest S/N ratio always
yields the optimum quality with minimum variance. Therefore, the level with a higher value determines the optimum level of each factor. For the experimentation carried out the best results would be obtained for AISI 52100, the workpiece material, infeed rate of 10 Âµm, and flow rate of 60mm3/min.
Main Effects Plot (data means) for SN ratios
Material In feed, Âµm
7.2
6.9
Mean of SN ratios
Mean of SN ratios
6.6
6.3
6.0
Fig. 10. Main Effects Plot (data means) for Means for Boric Acid

The regression Analysis
Taguchi technique can be used for the prediction of the full factorial design. The regression equation from full factorial prediction is
Ra = 0.451 – 0.000050 s + 0.000417 d – 0.000333 f
Where, Ra is the grinding force in Newton, s is the shear stress of workpiece material in MPa, d is the infeed in micrometers and f is flow rate in mm3/min
Flow Rate mm3/min
Flow Rate mm3/min
1 2 3
7.2
6.9
6.6
6.3
6.0
1 2 3
1 2 3
Table13. Regression Table
Predictor
Coefficient
Standard Error Coefficient
T
P
Constant
0.45089
0.01640
27.50
0.000
s, MPa
0.00005012
0.00001261
3.98
0.000
d, Âµm
0.0004167
0.0002891
1.44
0.000
F,
mm3/min
0.0003333
0.0002891
1.15
0.000
Predictor
Coefficient
Standard Error Coefficient
T
P
Constant
0.45089
0.01640
27.50
0.000
s, MPa
0.00005012
0.00001261
3.98
0.000
d, Âµm
0.0004167
0.0002891
1.44
0.000
F,
mm3/min
0.0003333
0.0002891
1.15
0.000
Signaltonoise: Smaller is better
Fig. 9. Main Effects Plot (data means) for SN rations (Signal to Noise: Smaller is better) for Boric Acid
From the calculated values of means, the values are obtained for the various levels of the factors and tabulated in Table 12 and the same is represented graphically in the Fig. 10.
Table 12 Response Table for Means
S = 0.00190413 RSq = 94.0% RSq(adj) = 93.3%

Analysis of variance
ANOVA, the analysis of variance is carried out and the results are included in Table 14.
In feed,
Flow rate
Table 14 ANOVA table
Level
Material
Âµm
mm3/min
1
0.4333
0.4656
0.4822
2
0.4900
0.4756
0.4722
3
0.5000
0.4822
0.4689
Delta
0.0667
0.0167
0.0133
Rank
1
2
3
Source
Degrees of Freedom
Sum of Squares
MS
F
Valu e
P
Valu e
Regres sion
3
0.0115
60
0.0038
53
6.4
0.00
0
Residu al error
23
0.0138
39
0.0006
0
Total
26
0.0254
0
Source
Degrees of Freedom
Sum of Squares
MS
F
Valu e
P
Valu e
Regres sion
3
0.0115
60
0.0038
53
6.4
0.00
0
Residu al error
23
0.0138
39
0.0006
0
Total
26
0.0254
0
Main Effects Plot (data means) for Means
Material In feed, Âµm
Source
Degrees of Freedom
Seq. Sum of Squares
s, MPa
1
0.0095108
d, Âµm
1
0.0012500
F, mm3/min
1
0.0008000
Source
Degrees of Freedom
Seq. Sum of Squares
s, MPa
1
0.0095108
d, Âµm
1
0.0012500
F, mm3/min
1
0.0008000
0.50
0.48
Mean of Means
Mean of Means
0.46
0.44
Flow Rate mm3/min
Flow Rate mm3/min
1 2
0.50
0.48
0.46
0.44
3 1 2 3
1 2 3


Interpreted Graphs For Surface finish As A Quality Characteristic
On the basis of the interpreted results of surface roughness obtained in grinding operation with the help of boric acid as solid lubricant for the materials selected, AISI 4140, AISI 4340 and AISI 52100 are given in Fig. 11 to Fig. 13
AISI 4140, Boric Acid as lubricant
AISI 4140, Boric Acid as lubricant
0.45
0.44
0.45
0.44
Ra, Âµm
Ra, Âµm
0.43
0.43
0.42
55
0.42
55
20
25
Infeed, Âµm
20
25
Infeed, Âµm
10
Flow Rate, mm3/min
10
Flow Rate, mm3/min
40
40
40
40
60
60
Fig. 11. AISI 4140, Boric Acid as lubricant
AISI 4340, Boric Acid as Lubricant
AISI 4340, Boric Acid as Lubricant
0.51
0.50
0.51
0.50
Ra, Âµm
0.49
0.48
55
Ra, Âµm
0.49
0.48
55
20
40
40
25 Infeed, Âµm
20
40
40
25 Infeed, Âµm
Flow Rate, mm3/min
60
Flow Rate, mm3/min
60
10
10
Fig. 12. AISI 4340, Boric Acid as lubricant
AISI 52100, Boric Acid as Lubricant
AISI 52100, Boric Acid as Lubricant
0.52
0.51
0.52
0.51
Ra ,Âµm
0.50
0.49
55
Ra ,Âµm
0.50
0.49
55
20
40
40
25 Infeed, Âµm
20
40
40
25 Infeed, Âµm
Flow Rate, mm3/min
60
Flow Rate, mm3/min
60
10
10
Fig. 13. AISI 52100, Boric Acid as lubricant

Conclusions
The experimental setup developed for the application of boric acid as a means to reduce the heat generated in the grinding zone has been investigated for improving the surface finish and reducing the cutting forces involved in the process. The Taguchi method of experimental design is used for the investigations.
When compared with the dry grinding and grinding with the conventional coolants, boric acid gives considerably reduces the force required for grinding operation and the surface finished improved. Hence the effective role of boric acid as a lubricant was evident from the improvement of output parameters.
Similar experimentation can be carried out with other solid lubricants like molybdenum disulphide, zinc phosphate and graphite, which may lead to most effective solid lubricant for surface grinding.

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