DMAIC methodology for optimizing honing process parameters

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 5, Issue 1, January (2014), © IAEME
68
EXPERIMENTAL VERIFICATION OF HONING PROCESS PARAMETERS
ON SURFACE ROUGHNESS WITH FORM TALYSURF 120 MACHINE IN
TAPER BEARINGS
Nikhil Balkrishna Bole
B.E. Mechanical, Genba Sopanrao Moze College of engineering, Pune, India;
Rohansing R. Kait
B.E. Mechanical, Genba Sopanrao Moze College of engineering, Pune, India;
ABSTRACT
Honing is an abrasive machining process that produces a precision surface on a metal work
piece by scrubbing an abrasive stone against it along a controlled path.
Honing is primarily used to improve the geometric form of a surface, but may also improve
the surface texture.
In this paper various parameters that affect the honing operations are discussed and the
methods to improve the surface finishing are studied on TalySurf machine using DMAIC
methodology
1. INTRODUCTION
Honing is the process of surface finishing. Usually honing is the last process in
manufacturing of components like bearings. Honing uses a special tool, called a honing stone or
a hone, to achieve a precision surface. The output of the honing process is normally given by the
factor called surface roughness factor and denoted by symbol Ra. The various parameters affect the
honing operation and the change of parameters can change the output by significant margin.
The mechanics of honing machine is quite simple the honing stone or hone that is used for
honing is pressed against a work piece to remove the necessary amount of material. Normally very
few amount of material is removed in the honing process, after the removal of material surface
finishing is done by slowly moving the stone over the work piece.
Honing can also be considered to be grinding in many manners. But during the grinding
process the grinding wheel moves along the pre defined path and any inaccuracies in the geometric
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 5, Issue 1, January (2014), pp. 68-78
© IAEME: www.iaeme.com/ijmet.asp
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69
shape of the grinding wheel will be transferred onto the part. Therefore, the accuracy of the finished
workpiece geometry is limited to the accuracy of the truing dresser. The accuracy becomes even
worse as the grind wheel wears, so truing must occur periodically to reshape it. While the honing
process occurs along the controlled path and more precision is acquired using honing process.
2. SIX SIGMA
Six Sigma at many organizations simply means a measure of quality that strives for near
perfection. Six Sigma is a disciplined, data-driven approach and methodology for eliminating defects
in any process – from manufacturing to transactional and from product to service.
The statistical representation of Six Sigma describes quantitatively how a process is
performing. To achieve Six Sigma, a process must not produce more than 3.4 defects per million
opportunities. A Six Sigma defect is defined as anything outside of customer specifications. A Six
Sigma opportunity is then the total quantity of chances for a defect. The fundamental objective of the
Six Sigma methodology is the implementation of a measurement-based strategy that focuses on
process improvement and variation reduction through the application of Six Sigma improvement
projects. This is accomplished through the use of two Six Sigma sub-methodologies: DMAIC and
DMADV. The Six Sigma DMAIC processes (define measure, analyze, improve, control) is an
improvement system for existing processes falling below specification and looking for incremental
improvement. The Six Sigma DMADV process (define, measure, analyze, design, verify) is an
improvement system used to develop new processes or products at Six Sigma quality levels. It can
also be employed if a current process requires more than just incremental improvement. Both Six
Sigma processes are executed by Six Sigma Green Belts and Six Sigma Black Belts, and are
overseen by Six Sigma Master Black Belts.
3. DMAIC METHODOLOGY
The DMAIC toolkit is without question the most effective process improvement framework
known in industry today. DMAIC is the five-step approach that makes up the Six Sigma tool kit, and
its sole objective is to drive costly variation from manufacturing and business processes. The five
steps in DMAIC are Define, Measure, Analyze, Improve, and Control. As the backbone of the Six
Sigma methodology, DMAIC delivers sustained defect-free performance and highly competitive
quality costs over the long run.
Simply stated, the leadership team in a given business Defines which metrics are most
important, Measures historical performance, Analyzes top opportunity areas for improving each
metric, Improves underlying processes with targeted DMAIC projects, and supports the long
term Controls identified by Six Sigma project teams. Only a handful of basic tools are required
when applying the DMAIC methodology to business metrics.
Each step in the cyclical DMAIC Process is required to ensure the best possible results. The
process steps:
Define the Customer, their Critical to Quality (CTQ) issues, and the Core Business Process involved.
Define who customers are, what their requirements are for products and services, and what their
expectations are
Define project boundaries the stop and start of the process
Define the process to be improved by mapping the process flow
Measure the performance of the Core Business Process involved.
Develop a data collection plan for the process

70
Collect data from many sources to determine types of defects and metrics
Compare to customer survey results to determine shortfall
Analyze the data collected and process map to determine root causes of defects and opportunities for
improvement.
Identify gaps between current performance and goal performance
Prioritize opportunities to improve
Identify sources of variation
Improve the target process by designing creative solutions to fix and prevent problems.
Create innovate solutions using technology and discipline
Develop and deploy implementation plan
Control the improvements to keep the process on the new course.
Prevent reverting back to the “old way”
Require the development, documentation and implementation of an ongoing monitoring plan
Institutionalize the improvements through the modification of systems and structures
4. PROCESS PARAMETERS IN HONING
i) Operation time: it is the time for which the honing process is performed on the work piece
ii) Honing stone Pressure: The pressure with which the honing stone is pressed against the work
piece is called honing stone pressure.
iii) RPM: Rotational speed of machine.
iv) Clamping pressure: Pressure with which the work piece is clamped to the machine.
v) Oscillation frequency: it is the frequency of honing stone oscillations. During the honing the
honing stone keeps on vibrating at high frequency.
vi) Surface profile: The surface profile plays a major role in the output of honing. It is observed
that some types of surface profile increase the Ra value.
vii) Coolant flow rate: The rate of flow of coolant. This rate plays significant role as it helps
maintain the temperature of the work piece during the machining.
viii) Feed rate: The rate at which the material is removed is call feed rate. The honing is normally
done with less feed rate as high feed rate can ruin the surface finishing at times.
5. SURFACE ROUGHNESS
Characterization of surface topography is important in applications involving friction,
lubrication, and wear. In general, it has been found that friction increases with average roughness.
Roughness parameters are, therefore, important in applications such as automobile brake linings,
floor surfaces, and tires. The effect of roughness on lubrication has also been studied to determine its
impact on issues regarding lubrication of sliding surfaces, compliant surfaces, and roller bearing
fatigue.
Finally, some researchers have found a correlation between initial roughness of sliding
surfaces and their wear rate. Such correlations have been used to predict failure time of contact
surfaces.
Another area where surface roughness plays a critical role is contact resistance. Thermal or
electrical conduction between two surfaces in contact occurs only through certain regions. In the case
of thermal conduction, for example, the heat flow lines are squeezed together at the areas of contact,
which results in a distortion of the isothermal lines.
Surface roughness, often shortened to roughness, is a measure of the texture of a surface. It
is quantified by the vertical deviations of a real surface from its ideal form. If these deviations are

71
large, the surface is rough; if they are small the surface is smooth. Roughness is typically considered
to be the high frequency, short wavelength component of a measured surface.
Roughness plays an important role in determining how a real object will interact with its
environment. Rough surfaces usually wear more quickly and have higher friction coefficients than
smooth surfaces. Roughness is often a good predictor of the performance of a mechanical
component, since irregularities in the surface may form nucleation sites for cracks or corrosion. On
the other hand, roughness may promote adhesion.
The characterization of surface roughness can be done in two principal planes. Using a
sinusoidal curve as a simplified model of the surface profile, roughness can be measured at right
angles to the surface in terms of the wave amplitude, and parallel to the surface in terms of the
surface wavelength. The latter one is also recognized as texture. The technique used to measure
roughness in any of these two planes will inevitably have certain limitations. The smallest amplitude
and wavelength that the instrument can detect correspond to its vertical and horizontal resolution,
respectively. Similarly, the largest amplitude and wavelength that can be measured by the instrument
are the vertical and horizontal range.
The first amplitude parameter used for roughness measurements was the vertical distance
between the highest peak and the lowest valley of the unfiltered profile, Pt. The designation of this
parameter was subsequently changed to Rt when electrical filters were incorporated. This parameter
was further divided into Rp and Rm, as illustrated in Figure 1.
Figure 1
Since it was mentioned that the concept of roughness has statistical implications, the next
step in the development of roughness parameters was to obtain an average parameter of roughness.
Thus, the RMS roughness was defined as follows:
where,
L = evaluation length
z = height
x = distance along measurement

72
In Europe, the signal of the instrument is passed through a rectifier in order to charge up a
capacitor. As a result, the output of the instrument is the centre-line average (CLA) roughness:
Figure 2-Arithmetic Average Roughness value
The next step in the realization of the statistical nature of roughness is to consider the
distribution of heights p(z), as shown in Figure 3.
Figure 3
The parameters used to characterize such distributions were the central moments, defined by
the following expression.
The second moment is known as the variance and represents the deviation of the distribution
from its mean. Taking the square root of the variance results in the standard deviation, which is
numerically identical to the RMS roughness. The third moment is the skewness and is a measure of
the asymmetry of the distribution. Finally, the fourth moment is known as the kurtosis and represents
the shape of the distribution curve.
In addition to amplitude parameters, there are other parameters that are used to characterize
texture. One of them is the high-spot count (HSC), which is the number of peaks per unit length. Its
reciprocal, Sm, is the mean spacing between peaks. Another parameter used to evaluate texture is the
profile length ratio RL, which is the length of the profile divided by its nominal length. Other
parameters found in the literature have not received popular acceptance.

73
The Centre Line Average Roughness is most common and we are using the same for the
discussion.
Table 1- Surface Roughness Expected From manufacturing processes
6. MEASUREMENT TECHNIQUES
Stylus Instruments
Stylus instruments are based on the principle of running a probe across a surface in order to
detect variations in height as a function of distance. One of the early stylus instruments employed a
system of levers to magnify the vertical displacement of the stylus and recorded the profile on a
smoked-glass plate. A schematic representation of this instrument is depicted in Figure 4.

74
Figure-4
The next step in the development of the stylus instruments was to incorporate a transducer,
which converted vertical displacement into an electrical signal. This signal can then be processed by
the instrument electronics to calculate a suitable roughness parameter. The type of transducer used
largely affects instrument performance. A piezoelectric crystal is often used as the transducer in the
less expensive instruments. Other transducer mechanisms include moving coil transducers,
capacitance transducers, and linear variable differential transformers (LVDT). The resolution of a
stylus instrument depends on its manufacturer and model. The Tencor P2 (SP-P2), for instance, has a
horizontal resolution of 0.02 mm, while the Tencor alpha-step 200 (SP-α200) has a horizontal
resolution of 0.04 mm.
Some error can be introduced in roughness measurements when a stylus instrument is used
because of several factors. Some of these factors are the size of the stylus, stylus load, stylus speed,
and lateral deflection by asperities. The effect of stylus size is illustrated in Figure 5, which is a
schematic comparison of an actual profile against the traced profile.
Figure 5
This effect becomes more significant as the curvature of the peaks and valleys decreases, or
the magnitude of the slope increases.
Regarding the effect of stylus load, it has been found that plastic deformation can be induced
on the surface if a load much higher than the recommended by the standards is used (Thomas, 1999).
It has been argued, however, that if plastic deformation occurs by the same magnitude everywhere
along the segment measured, the profile obtained is still representative of the original surface. One

75
study showed that profile measurements across the same segment under different loads resulted in
very similar profiles. The profiles obtained are presented in Figure 6.
Figure 6
7. TALYSURF MACHINE
A diamond stylus is moved vertically in contact with a sample and then moved laterally
across the sample for a specified distance and specified contact force. A profilometer can measure
small surface variations in vertical stylus displacement as a function of position.
Figure 7- Talysurf machine
7.1 Machine Specifications
i. Roughness Parameters : Ra, Rz, Ry, Rq (RMS), Rt, Rp, Rmax, Rm, R3z, S, Sm, Sk, tp
ii. Assessed profiles : Primary profile (P), Roughness profile (R) tp curve (material ratio Mr)
iii. Profile recording magnification : Vv:200 x - 20000 x Vh:20 x , 50 x
iv. Standard Conform : ISO/DIN/JIS/ANSI
v. Measuring mode : Metric ( m) and imperial ( inch)
vi. Display resolution: 0.001 _ m/0.04 –inch
vii. Display: LCD 128 x 64 dot-matrix, with backlight
viii. Dimensions of LCD : 50 x 30mm screen

76
ix. Display features : Detector stylus position indicator Battery level indicator Direct display of
parameters & profiles Direct printing LCD brightness adjustment Auto-off after 5 minutes
with auto-store Calibration through software (each cut-off)
x. Display languages : English, German, French, Italian, Spanish, Dutchxi. Data output : RS232;
direct to printer TA220 or PC
xi. Range : Ra, Rq:0.01-40 micro m Rz, Ry, Rp, Rt, R3z:0.02 -160_m Sm, S: 2-4000 _ m tp:1-
100%(%Ry) 13
xii. Detector : inductive, Diamond tip radius 5 _ m
8. CONCLUSION AND RESULT
Recommended Values of Parameters are as follows.
The aim is to increase the value of surface finishing by decreasing the value of Ra. The
various results and conclusions after experiments and the measurement of the Ra values are as
follows.
Oscillation Angle
With the increase in the oscillation angle the value of Ra seem to decrease with very low slope.
Stone Pressure
With the increase in stone pressure to the optimal limit the finishing of the work piece
increased so does the improvement in the surface roughness parameter Ra. This is because high stone
pressure means high surface finishing due to better machining of the surface.
R Code Register Description Unit Value Range(+,-)
R 100 Honing time 1 Sec 5.0 1.0
R 110 Workhead Motor Speed 1 rpm 450 100
R 130 Stone Pressure Bar 2 +1
R 130 Clamping Pressure Bar 5 +1
R 120 Oscillation rpm 1 rpm 500 100

77
Clamping Pressure
During the finishing process the clamping pressure seem to have not much effect on the Ra
value though it shows some change in the Ra value with the increase in clamping pressure. The
reason being high clamping pressure can cause damage to the work piece material and hence the
optimal limit is to be kept but if the clamping pressure is kept varying between the optimal limit then
it does not affect the machining process significantly.
Work Piece Speed
With the increase in the speed of work piece surface finishing improved and so does the
improvement in Ra is observed. This is mainly because the increase in rotation of work piece allows
more no of rotations in per minute and honing stone can perform its operation more times thus
increasing its surface finishing.
Oscillation Speed
The Ra value increases with the increase in oscillation speed so it is required to have less
oscillation speed in order to improve the surface finishing
Honing Time
With the increase in the process time the surface roughness increased significantly hence it is
good if the honing is done for shorter period of time.
Coolant Feed rate
Coolant feed rate played important role in keeping the material cool during the process. High
heat generation can increase the impact of honing stone on the work piece and in the end can ruin the
surface finishing hence sufficient feed rate is required to be maintained.
9. OUTPUT FROM THE EXPERIMENTS
Result
With the particular changes in the parameters the value of surface roughness parameter Ra
can be changed using DMAIC methodology.
Sr No. Ra Microns Average
1 2 3
1 0.15 0.149 0.151 0.15
2 0.147 0.152 0.155 0.152
3 0.14 0.141 0.139 0.14
4 0.154 0.150 0.146 0.15
5 0.16 0.158 0.159 0.159
6 0.144 0.143 0.145 0.144
7 0.148 0.145 0.148 0.147
8 0.158 0.158 0.161 0.159
9 0.156 0.154 0.155 0.155
10 0.159 0.160 0.161 0.160

78
10. REFERENCES
[1] International Journal of Quality research UDK- 6585.5012.7(497.11) Original Scientific
Paper (1.01).
[2] Int. J. Six Sigma and Competitive Advantage, Vol. 6, Nos. 1/2, 2010.
[3] International Journal of Mechanical and Production Engineering Research and Development
(IJMPERD) ISSN 2249-6890 Vol. 3, Issue 4, Oct 2013, 11-22.
[4] M. V. Ardeshana and N. L. Mehta, “Design of Punch and Die for Taper Roller Bearing Cage
for Multi Pocketing”, International Journal of Mechanical Engineering & Technology
(IJMET), Volume 4, Issue 2, 2013, pp. 367 - 372, ISSN Print: 0976 – 6340, ISSN Online:
0976 – 6359.
[5] Proceedings of the World Congress on Engineering and Computer Science 2008.
[6] Gunwant D.Shelake, Harshal K. Chavan, Prof. R. R. Deshmukh and Dr. S. D. Deshmukh,
“Modeling and Analysis of Surface Grinding for Residual Stresses in Workpiece”,
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[7] G.V.S.S.Sharma, Dr.P.S.Rao, V.Jagadeesh, and Amit Vishwakarma, “Process Capability
Improvement – A Case Study of Crank-Pin-Bore Honing Operation of an Engine Connecting
Rod Manufacturing Process”, International Journal of Advanced Research in Engineering &
Technology (IJARET), Volume 4, Issue 6, 2013, pp. 84 - 97, ISSN Print: 0976-6480,
ISSN Online: 0976-6499.
[8] WCECS 2008, October 22 - 24, 2008, San Francisco, USA.
[9] Morgan, J. [Jan, 2001]. Six Sigma meets ISO 9000. Quality world, Vol 2, Issue 1, pp. 22-24.
[10] Surface Roughness Ron Amaral Leonel Ho Chong December 2, 2002 San Jose State
University.
[11] Thomas, T.R., Rough Surfaces, 2nd ed., Imperial College Press, London (1999).
[12] Thomas, T.R., “Trends in surface roughness”, International Journal of Machine Tools and
Manufacture, 38, Issues 5-6, pp 405-411 (1998).

DMAIC methodology for optimizing honing process parameters

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