 Open Access
 Total Downloads : 1212
 Authors : Dilip T. Mehta, Prof. M. G. Rathi
 Paper ID : IJERTV2IS4327
 Volume & Issue : Volume 02, Issue 04 (April 2013)
 Published (First Online): 10042013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Effect Of Process Parameters On Material Removal Rate In Internal Gear Honing
International Journal of Engineering Research & Technology (IJERT)
ISSN: 22780181
Vol. 2 Issue 4, April – 2013
Dilip T. Mehta Prof. M. G. Rathi
ME Scholar, Mechanical Engineering Asst. Professor, Mechanical Engineering
Department, Government College of Department, Government College of Engineering, Aurangabad (MS) Engineering, Aurangabad (MS)
Abstract
Abrasive machining is one of the key technologies in gear manufacturing. Amongst abrasive finishing processes of hardened gears, internal gear honing process characterized by cutting speeds as low as 0.35 m/s in order to obtain fine finishing of gear tooth flank. A lot of effort has been spent to increase the productivity of such low speed internal gear honing process. From the discussion with company peoples and with the help of research paper it strongly felt that productivity of internal gear honing process bears a direct relationship with material removal rate. In this investigation, an effective approach based on Taguchi method, analysis of variance (ANOVA), multivariable linear regression (MVLR), has been developed to determine the optimum conditions leading to higher material removal rate. Experiments were conducted by varying wheel speed, honing amount, machining time using L9 orthogonal array of Taguchi method. The present work aims at optimizing internal gear honing process parameters to achieve high material removal rate without affecting surface quality. Experimental results from the orthogonal array were used as the training data for the MVLR model to map the relationship between process parameters and material removal rate. The experiment was conducted on primary helical gear of Bajaj Disc 125 two wheeler having material 16MnCr5H steel. From the investigation it concludes that machining time is most influencing parameter followed by wheel speed and honing amount on material removal rate (MRR).
Keywords: ANOVA; Internal gear honing; MRR; MVLR analysis.

Introduction
Gear honing was applied for the first time in the 70s of the last century to remove transportation damages on hardened tooth flanks. The honing distinguishes from established tooth flank grinding processes in the kinematics and the tool, which usually has a gear shape. Internal gear honing is a crossedaxis, fine, hard finishing process that uses pressure and abrasive honing wheel with geometrically undefined cutting edges to remove material along the tooth flanks in order to improve the surface finish and improves run out,
lead and profile characteristics[1]. It performs the simultaneously rotational and reciprocation movement. The process is similar to shaving in that a crossedaxis setup is used to produce the sliding velocity necessary to remove stock means of high pressure, sliding action and an abrasive honing wheel [2]. Generally a conventional gear honing is done after gear profile grinding process for fine finishing of gear flank. The stock remove in conventional honing process is 5 10 Âµm per tooth flank. But now a day a high performance internal gear honing is done directly after case hardening with increased stock removal as a substitute for gear grinding and conventional gear honing. This process also known as Direct honing or Power honing. About 20 40 Âµm per flank material remove in power honing [3]. The primary Benefits of internal gear honing are the higher contact surfaces offered by rolling a given gear with an internal honing wheel. Due to the higher overlapping ratio and the resulting better force distribution the achievable honing quality is more constant[4]. The cutting speed has an axial component in the tooth trace direction and a tangential component in the profile direction. This produces a uprootoriented surface structure that has improved noise qualities [5]. The advantages of the low cutting velocities is the induction of high residual compressive stresses on the flank surface which results in greater wear resistance and a longer gear lifetime, also there is no thermal damage of gear tooth[6]. In internal gear honing the cutting speed is very low i.e from 0.3 5 m/s which results in achieve very high quality of surface finish ranges from Rz= 25 Âµm with very low material removal rate which affect the productivity of process[7].
So that in this investigation we have increase the productivity of internal gear honing process by increasing material removal rate without affecting the quality of surface finish. During complete investigations, after honing process check the surface quality of lead and profile of gear teeth with the help of graph which is plotted by maag gear testing machine, the aim was to achieve the smallest possible cycle time along with the required surface quality. Before going to the main experimentation, some discussion with company peoples and with the help of research paper I was selected three input parameter like wheel speed, honing amount, machining time. By performing OVAT ( One Variable At a Time) analysis it is
clear that wheel speed, honing amount, machining time are influencing parameters on MRR and selected three levels for each parameter according to results. According to OVAT analysis following input parameters namely wheel speed, honing amount, machining time are selected by keeping other process parameters constant which are less influencing on material removal rate.
Figure 1 Surface structure of honed gear
Figure 2 Internal gear
honing

Experimental details

Design of experiments
Taguchi and Konishi had developed Taguchi techniques[8]. These techniques have been utilized widely in engineering analysis to optimize the performance characteristics within the combination of design parameters. Taguchi technique is also power tool for the design of high quality systems. It introduces an integrated approach that is simple and efficient to find the best range of designs for quality, performance, and computational cost [9]. In this study we have consider 3 factors which affect majorly on quality characteristic such as (A) Wheel Speed , (B) Honing Amount ,(C) Machining Time. The design of experiment was carried out by Taguchi methodology using Minitab 14 software. In this technique the main objective is to optimize the material removal rate that is influenced by three input process parameters.

Selection of control factors
From the discussion with company peoples and with the help of research paper it strongly felt that the productivity of gear honing process bears a direct relationship with material removal rate. So that MRR in gms/sec are selected as response parameter for experimentation.

Selection of orthogonal array
Since 3 controllable factors and three levels of each factor were considered L9 (3**3) Orthogonal Array was selected for this investigation.

Experimental set up
A Series of experiment was conducted to evaluate the influence of internal gear honing process parameters on material removal rate. The test was carried out on K300 Fassler Gear Teeth Honing CNC Machine (Siemens Auto.). Internal geared honing wheel consist of a grit material is a mixture of refined aluminium oxide (70%) and Sol Gel Corundum (30%) with a grain size 180.The grains were bonded in synthetic resin. The modulus of elasticity of the alloy wheel is 21 N/m2 and it has a density of 2.45 g/cm3[10]. During gear honing, honing oil HON7was used. The experiment was conducted by keeping all other parameter constant. The constant parmeters was honing oil pressure (1.5 bar), radial infeed (2Âµm/stroke), rapid infeed end position (0.01mm), tailstock pressure (1KN) applied to clamp the gear during operation, hydraulic pressure (17.20 Kg/cmÂ²) applied to expand the fixture and hold the gear by exerting the pressure at bore diameter.
Figure 3 K3OO Gear teeth honing CNC machine (Siemens Auto.)

Work material
A primary helical gear of Bajaj Disc125 cc two wheeler selected for experimentation having material 16MnCr5H steel. The chemical
composition and hardness of the material is given in the table 1 below,
Table 1 Chemical Composition and Properties of 16MnCr5H Steel
16MnCr5H
Chemical elements
C
Si max
Mn
P
max
S
Cr
Chemical Composition (%)
0.14
0.19
0.4
1.00
1.30
0.035
0,035
0.80
1.10
Property
Value in metric unit
Density
7.872 Ã—10Â³
kg/mÂ³
Modulus of elasticity
205
GPa
Thermal expansion (20 ÂºC)
12.6Ã—106
ÂºCÂ¯Â¹
Specific heat capacity
452
J/(kgK)
Thermal conductivity
44.7
W/(mK)
Electric resistivity
2.28Ã—107
Ohmm
Surface hardness
79 – 84
HRA
Core hardness
29 – 43
HRC
The specification of helical gear is given in the table 2 below,
Table 2 Specification of helical gear
Two wheeler Disc125 helical gear specifications
Outer Diameter
120.3 – 120.5 mm
Number Of Teeth
75
Module
1.5
Helix Angle
17 Â°
Direction Of Helix Angle
Right
Pressure Angle
17.5Â°
Pitch Circle Diameter
117.64 mm
Over Pin Diameter
120.731 – 120.796 mm

Weight measurement
The weight of the gear taken before and after the experimentation to find out the material removal rate. For weight measurement digital balance machine was used of Phoenix Gold 300 P having maximum capacity of 300 grams and minimum 0.2 grams having error value up to 0.01 grams. The material removal rate is calculated by taking weight difference and divided it by machining time.


Experimental conditions
A series of experiment was carried out on K300 Fassler Gear Teeth Honing CNC Machine
(Siemens Auto.). From OVAT analysis three input controlling parameters selected having three levels. Details of parameters and their levels used shown in the table 3,
Table 3 Process parameters and levels
Notation
Process parameters
Level 1
Level 2
Level 3
A
Wheel Speed (rpm)
250
300
350
B
Honing Amount (mm)
0.01
0.02
0.03
C
Machining Time (sec)
25
30
35
The experimental design matrix is obtained by Taguchi methodology by using Minitab 14 software is shown in table 4 below,
Table 4 Layout for Experimental Design according to L9 Array
Exp. No.
A
Wheel Speed (rpm)
B
Honing Amount (mm)
C
Machining Time (sec)
1
250
0.01
25
2
250
0.02
30
3
250
0.03
35
4
300
0.01
30
5
300
0.02
35
6
300
0.03
25
7
350
0.01
35
8
350
0.02
25
9
350
0.03
30

Results and Discussion

S/N Ratio Analysis
In the Taguchi method, the term signal represents the desirable value (mean) for the output characteristic and the term noise represents the undesirable value for the output characteristic. Taguchi uses the S/N ratio to measure the quality characteristic deviating from the desired value. There are several S/N ratios available depending on type of characteristic: lower is better (LB), nominal is best (NB), or larger is better (LB). Larger is better S/N ratio used here because the quality characteristic is MRR. Larger thebetter quality characteristic was implemented and introduced in this study.
Level
Wheel Rotation
(rpm)
Honing Amount
(mm)
Machining Time
(sec)
1
28.43
28.1
26.77
2
27.27
27.79
27.55
3
27.05
26.85
28.42
Delta
1.38
1.25
1.65
Rank
2
3
1
Level
Wheel Rotation
(rpm)
Honing Amount
(mm)
Machining Time
(sec)
1
28.43
28.1
26.77
2
27.27
27.79
27.55
3
27.05
26.85
28.42
Delta
1.38
1.25
1.65
Rank
2
3
1
Larger the better characteristic, S/N = 10 log10 (MSD)
2 2 2
2 2 2
Where MSD= Mean Squared Division MSD = ( 1/Y1 + 1/Y2 + 1/Y3 +.)/n
Where Y1, Y2, Y3 are the responses and n is the number of tests in a trial and m is the target value of the result. Table 5 indicate avg. MRR and S/N ratios for different combinations of design matrix.
A combination of factors with highest S/N ratio
is the optimum situation where the MRR is maximum.
Table 5 summary Report for different trial conducted during Experimentation
Trial No.
MRR (gms/sec)
Avg. MRR
(gms/sec)
S/N Ratio
Trial 1
Trial 2/p>
Trial 3
1
0.0398
0.0391
0.0399
0.0396
28.0461
2
0.0377
0.0373
0.0368
0.0373
28.5658
3
0.0361
0.0374
0.037
0.0368
28.683
4
0.0401
0.0403
0.0404
0.0403
27.8939
5
0.0397
0.0396
0.037
0.0388
28.2234
6
0.0349
0.0356
0.035
0.052
25.6799
7
0.038
0.0376
0.039
0.0382
28.3587
8
0.0471
0.0468
0.0465
0.0468
26.5951
9
0.0349
0.0354
0.0343
0.049
26.1961
Table 6 Estimated Model Coefficient for SN ratios
Term
Coef
SE Coef
T
P
Constant
27.582
0.07203
382.926
0
Wheel Ro 250
0.8492
0.10187
8.336
0.014
Wheel Ro 300
0.3167
0.10187
3.109
0.09
Honing A 0.01
0.5171
0.10187
5.076
0.037
Honing A 0.02
0.2123
0.10187
2.084
0.173
Machining T 25
0.8087
0.10187
7.939
0.015
Machining T 30
0.0305
0.10187
0.3
0.793
Summary of Model S = 0.2161 RSq = 99.1%
RSq(adj) = 96.3%
Table 7 Response Table for Signal to Noise Ratios – Larger is better (MRR)
The level of a factor with the highest S/N ratio was the optimum level for responses measured. From the Table 7 and Figure 4 it is clear that, the optimum value levels for higher material removal rate are at a wheel speed (350 rpm), honing amount (0.03 mm), and machining time (25 sec). The response table includes ranks based on Delta statistics, which compare the relative magnitude of effects. The Delta statistic is the highest minus the lowest average for each factor. Minitab assigns ranks based on Delta values; rank one to the highest Delta value, rank two to the Second highest, and so on. From both ANOVA and response tables it is clear that the most significant factor is machining time (C), followed by wheel speed (A) and honing amount (B). Figure 4 shows graphically the effect of the three control factors on material removal rate.
Main Effects Plot (data means) for SN ratios
Wheel Rotation Honing Amount
Main Effects Plot (data means) for SN ratios
Wheel Rotation Honing Amount
27.0
27.5
28.0
28.5
250 300
Machine Time
350
0.01
0.02
0.03
27.0
27.5
28.0
28.5
250 300
Machine Time
350
0.01
0.02
0.03
25
30
35
25
30
35
Signaltonoise: Larger is better
Signaltonoise: Larger is better
27.0
27.5
28.0
28.5
27.0
27.5
28.0
28.5
Mean of SN ratios
Mean of SN ratios
Figure 4 Effect of process parameters on S/N Ratio

Analysis of Variance (ANOVA)
The purpose of ANOVA is to investigate which process parameters significantly affect the quality characteristic. The analysis of the experimental data is carried out using the software MINITAB 14
specially used for design of experiment applications. In order to find out statistical Significance of various factors like wheel speed (A), honing amount (B), and machining time(C), and their interactions on material removal rate, analysis of variance (ANOVA) is performed on experimental data. Table 8 shows the result of the ANOVA with the material removal rate. The last column of the table indicates pvalue for the individual control factors. It is known that smaller the pvalue, greater the significance of the factor. The ANOVA table for S/N ratio (Table 8) indicate that, the machining time (p=0.022), wheel speed (p= 0.027) and honing amount (p=0.036) in this order, are significant control factors effecting material removal rate. It means, the machining time is the most influencing factor and the honing amount has less influence on the performance output compared to machining time.
Table 8 Analysis of Variance for S/N ratios (Material Removal Rate)
Table 9 Optimum Condition and Percent Contribution
SR.
No.
Factors
Level Description
Level
Contribution (%)
1
A: Wheel Speed
350
3
33.08
2
B: Honing Amount
0.03
3
25.28
3
C:Machining Time
25
1
40.7
From the Table 8 it is clear that machining time is most influencing while honing amount is least influencing parameter on MRR.
Figure 5 Percentage contribution of machining parameters
Residual Plots for MRR (gms/sec)
Normal Probability Plot of the Residuals Residuals Versus the Fitted Values
Source
DF
Seq SS
Adj SS
Adj MS
F
P
Wheel Rotation (rpm)
2
3.315
3.315
1.658
35.5
0.03
Honing Amount (mm)
2
2.5337
2.534
1.267
27.1
0.04
Machining Time (sec)
2
4.0781
4.078
2.039
43.7
0.02
Residual Error
2
0.0934
0.093
0.047
Total
8
10.02
Source
DF
Seq SS
Adj SS
Adj MS
F
P
Wheel Rotation (rpm)
2
3.315
3.315
1.658
35.5
0.03
Honing Amount (mm)
2
2.5337
2.534
1.267
27.1
0.04
Machining Time (sec)
2
4.0781
4.078
2.039
43.7
0.02
Residual Error
2
0.0934
0.093
0.047
Total
8
10.02
99
Percent
Percent
90
50
10
1
0.004
0.002
0.000
Residual
0.002
0.004
0.0030
Residual
Residual
0.0015
0.0000
0.0015
0.0030
0.040
0.045
Fitted Value
0.050

Percent contribution
Percent contribution to the total sum of
Histogram of the Residuals Residuals Versus the Order of the Data
3 0.0030
Frequency
Frequency
Residual
Residual
0.0015
2
0.0000
1 0.0015
0 0.0030
square can be used to evaluate the importance of a
0.003 0.002 0.001 0.000 0.001 0.002 0.003
1 2 3 4
5 6 7 8 9
Residual Observation Order
change in the process parameter on these quality characteristics
Percent contribution (P) = (SSA / SST) *100
Figure 6 Residual Plots for MRR

Regression Analysis
Mathematical models for process parameters such as wheel speed, honing amount and machining time were obtained from regression analysis using MINITAB 14 statistical software to predict material removal rate. The regression equation is
Y= 0.0398 + 0.000068*A + 0.328*B – 0.000820*C
.. (1)
S = 0.00201025 RSq= 92.1% R Sq(adj)=87.3%
Where, Y = Response i.e. Material Removal Rate (gms/sec), A = Wheel Speed (rpm), B = Honing Amount (mm), C = Machining Time (sec). If we put optimum parameters which are drawn by ANOVA in equation 1 it will give optimum value of quality characteristic which is maximum MRR.
Yopt = 0.0398 + 0.000068*A3 + 0.328*B3 – 0.000820*C1
Y opt = 0.0398 + 0.000068* 350 + 0.328*0.03 – 0.000820*25
Y opt = 0.05294 gms/sec (Predicted by Regression Equation)
In multiple linear regression analysis, R2 is value of the correlation coefficient and should be between 0.8 and 1. In this study, results obtained from MRR in good agreement with regression models (R2>0.80).

Conformation Experiments
In Order to test the predicted result, confirmation experiment has been conducted by running another four trials at the optimal settings of the process parameters determined from the Analysis i.e. A3B3C1
Observation
Trial
Avg.
MRR
(gms/sec)
S/N
Ratio
1
2
3
1
0.0532
0.0528
0.053
0.053
25.5
The results are shown in above table and it is observed that the average MRR i.e. 0.053 gms/sec and average S/N Ratio 25.50 which falls within predicted 80% Confidence Interval


Conclusions
The Taguchi method was applied to find an optimal setting of the internal gear honing process. The result from the Taguchi method chooses an optimal solution from combinations of factors if it gives maximized normalized combined S/N ratio of targeted outputs. The L9 OA was used to accommodate three control factors and each with 3 levels for experimental plan selected process parameters are Wheel Speed (250, 300, 350 rpm),
Honing Amount (0.01, 0.02, 0.03 mm), Machining
Time (25, 30, 35 sec). The results are summarized as follows:

From the analysis, it is clear that the three process parameters, wheel speed, honing amount and machining
time have significant effect on material removal rate.

The analysis of variance proves that the most influencing parameters on MRR are machining time and wheel speed. While honing amount is least significant as compared to machining time and wheel speed.

The result of present investigation is valid within specified range of process parameters

Also the prediction made by Regression Analysis is in good agreement with Confirmation results.

The optimal levels of internal gear honing process parameters are found to be A3B3C1:
Wheel Speed (rpm) 350
Honing Amount (mm) 0.03
Machining Time (sec) 25
References

H. K. Tonshoff (l), T. Friemuth, C. Marzenell, Properties of Honed Gears During lifetime

John P. Dugas, Rotary Gear Honing

N. Amini, H. Westberg, F. Klocke, T. KÂ¨ollner, An experimental study on the effect of power honing on gear surface topography, Gear Technol. 16 (1) (1999) 1118

B. Karpuschewski (1)*, H.J. Knoche,
M. Hipke, Gear finishing by abrasive processes

Ned W. Wright & Herbert S'chriefer, Basic Honing and Advanced Free Form Honing

E. Brinksmeier, C. Schneider, Grinding at very low speeds, Ann. CIRP 46 (1) (1997) 223226

Brinksmeier E, Giwerzew A (2005) Hard Gear Finishing Viewed as a Process of Abrasive Wear. WearAn International Journal on the Science and Technology of`Friction Lubrication and Wear 258(14):6269

Taguchi G, Konishi S. Taguchi methods, orthogonal arrays and linear
graphs, tools for quality American supplier institute. American Supplier Institute; 1987 [p. 835].

Taguchi G. Introduction to quality engineering. New York: Mc GrawHill; 1990

Hermes Abrasives, Gear Honing Technology CatÃ¡logo TÃ©cnico, 2008. 54, p. 110