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International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 6, September – October (2013), © IAEME
84
PROCESS CAPABILITY IMPROVEMENT – A CASE STUDY OF CRANK-
PIN-BORE HONING OPERATION OF AN ENGINE CONNECTING ROD
MANUFACTURING PROCESS
G.V.S.S.Sharma1*
, Dr.P.S.Rao2, V.Jagadeesh
3, and Amit Vishwakarma
4
1Assistant Professor, Dept. of Mechanical Engg., GMR Institute of Technology, Rajam, A.P.India.
2Professor, Industrial Engineering Dept., GITAM University,Visakhapatnam, A.P., India.
3Assistant Professor, Dept. of Mechanical Engg., GMR Institute of Technology, Rajam,A.P. India.
4Manager, Manufacturing Engineering, Volvo Eicher Commercial Vehicles Limted,
Pithampur,M.P.,India
ABSTRACT
This paper illustrates on crank-pin-bore honing, critical to quality characteristic of the
connecting rod manufacturing of internal combustion engine. Here the procedure for attainment of
the Cp and Cpk values greater than 1.33 is elaborated by identifying the root cause through the quality
control tools like the cause and effect diagram and examining each cause one after another. In this
paper the DMAIC approach is employed (Define-Measure- Analyze-Improve-Control). The
Definition phase starts with the process mapping and identifying the CTQ characteristic. The next
phase is the measurement phase comprising of the cause and effect diagram and data collection of
CTQ characteristic measurements. Then follows the Analysis phase where the process potential and
performance capability indices are calculated, followed by the analysis of variance of the mean
values (ANOVA). Finally the process monitoring charts are used for controlling the process and
prevent any deviations. By using this DMAIC approach, standard deviation is reduced from 0.005 to
0.002 and the Cp values raised from 0.50 to 1.52 and Cpk values from 0.34 to 1.45 respectively.
Keywords: Cause and effect diagram, Critical to quality (CTQ) characteristic, statistical quality
control (SQC), process monitoring charts, Analysis of Variance (ANOVA)
1. INTRODUCTION
One of the major manufacturing processes in engine manufacturing is that of connecting rod
manufacturing. This paper implements the DMAIC approach [1],[9] i.e., Define-Measure-Analyze-
Improve-Control approach to improve the capability of connecting rod manufacturing process by
reducing the crank-pin bore diameter variations from a nominal value. Process mapping and
identifying CTQ is carried out in “Define” phase, while an estimate of process capability indices is
carried out in the “Measure phase”. One way ANOVA method of investigation to test for the
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differences between the manufacturing data is employed in the “Analysis phase”. Finally, the PMC
(process monitoring chart) for the thrust face thickness is employed in the “Improve and Control
phase”.
Statistical Quality Control studies form the basic tool for obtaining the required process
capability confidence levels. The various process capability indices [4] are defined as follows:
P
P K U
P K L
U S L L S LC (1 )
6
U S LC (2 )
3
L S LC
3
−=
σ
− µ=
σ
µ −=
σ
P K
(3 )
U S L L S LC m in , (4 )
3 3
w h ere ,
U S L an d L S L are U p per an d L ow er specificatio n lim its
µ = p rocess m ean , σ = standard deviation
− µ µ − =
σ σ
The term Cp denotes for the process potential capability index and similarly the term Cpk
denotes for the process performance capability index. Cp gives an indication of the dispersion of the
product dimensional values within the specified tolerance zone during the manufacturing process.
Similarly the index Cpk denotes for the centering of the manufacturing process with respect to the
mean of the specified dimensional tolerance zone of the product. Cpk gives us an idea that whether the
manufacturing process is performing at the middle of the tolerance zone or nearer to the upper or
lower tolerance limits. If the manufacturing process is nearer to the lower limit then the process
performance capability index is given by Cpkl and if the manufacturing process is nearer to the upper
limit then the process performance capability index is given by Cpku. As a measure of perceptional
safety the minimum value amongst the two is taken as the value of Cpk.
2. LITERATURE REVIEW
Schilling, E.G. [1], in 1994, emphasized on how the process control is better than the
traditional sampling techniques. During the same era, Locke and John.W. [2], in their paper titled
“Statistical Measurement Control”, emphasized on the importance of process charts, cause and effect
considerations, and control charting. After primitive studies on statistical quality control, Hung-Chin
Lin [3] in 2004, had thrown some light on process capability indices for normal distribution.
J.P.C.Tong et al.[4] suggested that how a Define-Measure-Analyze-Improve-Control (DMAIC)
approach is useful for printed circuit board quality improvement. They also proved that how Design-
Of-Experiments is one of the core statistical tools for six-sigma improvement. Subsequently, Ming-
Hsien Caleb Li et al.[5] once again proved the importance of DMAIC approach to improve the
capability of surface mount technology in solder printing process. Yeong-Dong Hwang [6] in their
paper discussed the DMAIC phases in detail with application to manufacturing execution system.
Enzo Gentili et al. [7], applied the DMAIC process for a mechanical manufacturing process line,
which manufactures both professional and simple kitchen knives. Chittaranjan Sahey et al. [8], once
again brought the DMAIC approach into use for analyzing the manufacturing lines of a brake lever at
a Connecticut automotive components manufacturing company. Rupinder Singh [9], investigated the
process capability of polyjet printing for plastic components. In his observation, he voyaged the
improvement journey of the process of critical dimensions and their Cpk values attainment greater than
1.33, which is considered to be industrial benchmark. In recent studies conducted by S.J.Lin et al.
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[10], they focused on turbine engine blade inspection, as it is a key aspect of engine quality. They
elaborated on the accurate yield assessment of the processes of multiple characteristics like the turbine
blades manufacturing process. A. Kumaravadivel and U. Natarajan [11] dealt with application of six-
sigma methodology of the flywheel casting process. The primary problem-solving tools used were the
process-map, cause and effect matrix and failure modes and effects analysis (FMEA).
A careful study from the above literature reveals that the DMAIC approach is the best problem
solving tools for improving the manufacturing process capability levels. Hence, this paper focuses on
the application of DMAIC approach for process capability improvement of the crank-pin bore honing
operation of an engine connecting rod manufacturing process.
3. DEFINE PHASE
3.1 Process Mapping The define phase starts with the correct mapping of the machining process flow of the
connecting rod. The process flow chart for machining line of the connecting rod machining cell
consisted of the following machining operations sequence, as shown in the Fig.1 below :-
Fig. 1: Process Flow chart
The Table 1 below depicts the description of the machining operations of connecting rod
manufacturing cell.
Table 1: Machining operations of connecting rod manufacturing cell.
Machining
Operation no. Description
10 Thrust face rough grinding
20 Gudgeon pin rough boring
30 Crank pin rough boring
40 Side face broaching
50 Finish grinding
60 Bolt hole drilling
70 Key way milling
80 Rod and cap assembly
90 Finish grinding of assembly
100 Finish boring of gudgeon pin
110 Finish boring of crank pin
120 Crank pin bore Honing
130 Magnetic crack detection
140 Final quality check set making and
dispatch to engine assembly line.
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3.2 Identifying CTQ characteristic The crank-pin bore (CP bore) diameter finishing and achieving the dimensional accuracy is
achieved by honing process. CP bore diameter form a very important dimensional characteristic as
goes into the assembly with the crank-pins of the crankshaft in the engine assembly. Any out-of-
dimension of the CP bore of the connecting rod leads to incorrect assembly with the crankshaft
thereby leading to engine failures and costly rework. Hence for achieving the desired engine
performance the CP bore of the connecting rod forms an important CTQ characteristic. In this
regard, this research aims at improving the connecting-rod manufacturing process by reducing the
CP bore diameter variations during honing operation so that these variations are not carried to the
subsequent set-making for selective assembly, down the engine assembly line.
The acceptable CP bore tolerance zone variation for honing of the connecting-rod cap and rod
assembly was limited to 0.015 microns. The connecting rods which were out of these tolerance limits
resulted in inaccurate set-making for selective assembly and subsequently rejected in the engine
assembly line. This caused costly repair and rework. Hence crank-pin bore diameter was of the main
concern and identified as a CTQ characteristic, whose value is equal to 85.000 (+0.085/+0.070)
mm. The
figure 1 below depicts the diagrammatic view of this CTQ characteristic.
Fig. 2: Crank-pin-bore diameter
4. MEASUREMENT PHASE
In this phase the data of crank-pin bore diameter of nominal 32 consecutive readings is
collected and plotted on the process monitoring chart. This data collection was performed in 3
iterations. In each iteration the data set of CP bore diameter measurement readings is taken and
analyzed for Cp, Cpk values and followed by suitable corrective action. After the corrective action is
implemented, the next iteration was performed. This procedure was continued until the Cp and Cpk
values are greater than or equal to 1.33, i.e., upto 4σ quality level as decided by the management of
the Engine manufacturing Plant.
4.1 Cause and Effect diagram:
The critical to quality characteristic identified was the crank pin bore diameter which is equal
to 85.000(+0.085/+0.070)
mm whose machining tolerance zone is equal to 0.015 mm. The Cp value, i.e.,
the process potential capability index,{Cp=(USL-LSL)/6σ}, nominally was equal to 0.50, which was
far below the acceptance level limit of greater than 1.33 for the above CTQ. The first part of the
measurement phase investigation was to track down and differentiate the common causes and special
causes involved. For doing so, the cause and effect diagram, [2][6], was employed. ,as show below in
Fig3
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Fig 3: Cause and Effect diagram showing the variables affecting the crank-pin bore diameter during
the engine connecting rod CP bore diameter honing operation
The machine employed was vertical honing machine. It consists of honing stones and a
wedge shaped mandrel housing the honing stones. Below shown is the figure of the honing tool
employed.
Fig 4: Honing Tool employing the honing stones
The machine was equipped with pneumatic ring gaging system for the manual in-process
measurements of the CTQ characteristic.
4.2 Process FMEA (failure modes and effects analysis)
FMEA sheet for CP bore honing is shown in Fig 5.
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Fig. 5 FMEA sheet
From the causes enumerated in the cause and effect diagram, the Failure modes and effects
analysis was performed and the corresponding FMEA sheet is displayed in the Fig.5. It can be
noticed that the highest RPN (Risk Priority Number) is for the incorrect honing stones presetting and
except for the geometric variations, rest all enumerated causes have an RPN greater than 100 and
hence are liable for improvement actions. The data collection was performed and the measurements
of the crank-pin bore diameter after honing operation were recorded for further analysis and for the
improvement of process capability of the connecting rod manufacturing process.
4.3 Data collection: Data collection of the critical to quality characteristic was performed for 32 consecutive
machined components.
Data collection was performed in 4 iterations spanning for a period of 3 weeks i.e., about
2500 consecutive components. The data is tabulated in the tabular form in the Table 2 as follows:
Table 2 Measured dimensions of CP bore in tabular form S.No. Iteration 1 Iteration 2 Iteration 3 S.No. Iteration 1 Iteration 2 Iteration 3
01 85.080 85.074 85.080 17 85.082 85.075 85.076
02 85.077 85.071 85.080 18 85.082 85.074 85.076
03 85.081 85.077 85.078 19 85.077 85.074 85.075
04 85.082 85.076 85.077 20 85.083 85.073 85.075
05 85.077 85.075 85.078 21 85.079 85.070 85.080
06 85.082 85.073 85.076 22 85.083 85.077 85.078
07 85.081 85.074 85.076 23 85.076 85.072 85.078
08 85.078 85.075 85.077 24 85.083 85.074 85.079
09 85.083 85.072 85.075 25 85.078 85.072 85.077
10 85.076 85.074 85.075 26 85.080 85.071 85.077
11 85.081 85.073 85.080 27 85.076 85.074 85.077
12 85.076 85.070 85.079 28 85.082 85.070 85.076
13 85.077 85.076 85.079 29 85.077 85.075 85.076
14 85.081 85.075 85.078 30 85.080 85.072 85.075
15 85.079 85.072 85.078 31 85.077 85.075 85.075
16 85.077 85.074 85.077 32 85.082 85.074 85.076
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The data in the above Table 2 was plotted on the process monitoring chart with no. of
components in x-axis and the dimension on y-axis, and is shown in the Fig 6 below
Fig.6 Process monitoring chart
5. ANALYSIS PHASE
The analysis phase comprises of performing the calculations for the Cp and Cpk values across
each iteration. This was followed by one way ANOVA method of investigation to test the differences
between the three iterations of the data sets.
5.1 Calculations of Cp and Cpk
The calculations of Cp and Cpk are tabulated as below in Table 3
Table 3 Calculations of Cp and Cpk
Formula Iteration 1 Iteration 2 Iteration 3
USL 85.085 85.085 85.085
LSL 85.070 85.070 85.070
σ 0.005 0.002 0.002
( )6
USL LSL
CP
σ
−= 0.50 1.07 1.52
( )3
USL MEAN
CPKU
σ
−= 0.34 1.60 1.59
( )3
MEAN LSL
CPKL
σ
−= 0.67 0.53 1.45
CPK = min (CPKU , CPKL) 0.34 0.53 1.45
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From the process monitoring charts and the calculations in Table 3, the following is the
analysis done for each iteration set of data:
5.1.1 Iteration no.1 The first set of the Statistical process capability study comprised of the raw data of the CTQ
characteristic, which depicted the transparent picture of the state of the existed problem. Continuous
set of readings of the connecting rod after the crank-pin bore honing operation no.120 were captured
with the help of a pneumatic gauge set up installed as an integral part of the honing machine for
performing the in-process inspection. Hence it is seen here in the 1st iteration of SPC studies that the
process is not capable and the Cp and Cpk values of the characteristic under study are 0.50 and 0.34,
which are far less than that for process to be capable, i.e., 1.33. Hence, next set of data is captured
after performing measurement system analysis (MSA) studies in the iteration 2 of SPC studies.
5.1.2 Iteration no. 2 In this Iteration, data is collected after the machine preventive maintenance schedule
completion and replacement of worn out honing stones of the honing machine.
From the above set of data from Table 3 it is seen that there is a significant increase of Cp from 0.50
to 1.07 and Cpk from 0.34 to 0.53. This marginal increase is a positive sign but still the process is not
capable as both Cp and Cpk are less than the desired value of 1.33. This calls for another iteration.
5.1.3 Iteration no.3 In this iteration the data is collected after gauge repeatability and reproducibility (GR&R)
performed for the pneumatic gauge and calibration of the pneumatic gauge as a part of the
measurement system analysis procedure.
From the above set of data from Table 3 it is seen that there is a noticeable increase of Cp
from 1.07 to 1.52 and Cpk from 0.53 to 1.45. Since both Cp and Cpk are greater than 1.33 hence the
honing machining process is declared as a capable process. Here the data is collected after manually
pre-setting the value for the tool-wear (honing stone wear) compensation at 05 microns, on the
pneumatic panel of the pneumatic gauge. This means that after every wear-out of 05 microns of the
honing stones, the pneumatic gage is caliberated with the pneumatic ring gage. This caliberation is
found necessary after every 10 components honed. Hence the pneumatic gage caliberation after
honing of every 10 components is instructed in the operator’s checklist as a part of standardization
procedure.
5.2 One way ANOVA method
The one way ANOVA method of investigation is adopted to test for the differences between
the three iterations of data collected.
5.2.1 Procedure describing one way ANOVA: In general, one way ANOVA technique is used to study the effect of k(>2) levels of a single
factor. A factor is a characteristic under consideration, thought to influence the measured
observations and level is a value of the factor.
To determine if different levels of the factor affect measured observations differently, the following
hypotheses are to be tested:
H0 : µ i = µ all i= 1,2,3,
H1 : µ i ≠ µ for some i= 1,2,3,where,
µ i is the population mean for level i , and
µ is the overall grand mean of all levels.
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Here we have 3 levels (i.e., 3 iterations) and each level consisting of 32 measurement
readings of crank-pin bore diameter of connecting rod. The sum, sum of squares, mean and variance
for each iteration is tabulated in the table 4 below:
Table 4: Mean and variance of all the three iterations
Formula Iteration 1 Iteration 2 Iteration 3
Sample size 32 32 32
Sum 2722.545 2722.354 2722.469
Sum of squares 231632.9 231600.35 231619.9
Mean (µ i) 85.079 85.0735 85.077
Variance (σ2) 0.0000062 0.0000035 0.0000027
If xij denote the data from the ith level and jth observation, then overall or grand mean is given by : 4 32
ij
i 1 j 1
x, (5)
N= =
µ =∑∑
Where N is the total sample size of all the three iterations i.e., 32x3=96
Hence, from equation (5), we get, µ = 85.076
The sum of squared deviations about the grand mean across all N observations is given by:
( )4 32
2
T ij
i 1 j 1
SST x (6)= =
= − µ∑∑
The sum of squared deviations for each level mean about the grand mean is given by:
( )4
2
L i
i 1
SST 4 (7)=
= × µ − µ∑
The sum of squared deviations for all observations within each level from that level mean,
summed across all levels is given by :-
( )4 32
2
E ij i
i 1 j 1
SST x (8)= =
= − µ∑∑
From equations (6), (7) and (8), the values of SSTT, SSTG and SSTE obtained are 90x10-5
,
52x10-5
and 40.4x10-5
respectively.
On dividing SSTT, SSTL and SSTE by their associated degrees of freedom (df), we get mean
of squared deviations respectively.
Hence, mean of squared deviations between levels is given by:
( )
55L
L
L
SST 52 10MST = = 26 10 (9)
df 3 1
−−×
= ×−
Mean of squared deviations within levels is given by:
( )
55E
E
E
SST 40.4 10MST = = 0.434 10 (10)
df 96 3
−−×
= ×−
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Finally, the F Statistic is given by the following formula: 5
LSTATISTIC 5
E
MST 26 10F = 59.9 (11)
MST 0.434 10
−
−
×= =
×
On summarizing all the above values in tabular form, the ANOVA table is obtained as shown
below in Table 5 :-
Table 5: ANOVA Table:
Source of
variation df
Sum of
squares
Mean of
squares F
Level 2 52x10-5 26x10-5 59.9
Within/error 93 40.4x10-5
0.434x10-5
Total 95 90x10-5
An α value of 0.05 is typically used, corresponding to 95% confidence levels. If α is defined
to be equal to 0.05, then, the critical value for rejection region is given by F CRITICAL (α, K-1, N-K). and
is obtained to be 3.094. Thus,
CRITICALF 3.094 (12)=
From equations (11) and (12) it is seen that:
STATISTIC CRITICALF F (13)>
Therefore, the decision will be to reject the null hypothesis. If the decision from the one-way
analysis of variance is to reject the null hypothesis, then, it indicates that at least one of the means (
µ i ) is different from the remaining other means. In order to figure out where this difference lie, a
post-hoc ANOVA test is required.
5.2.2 Post-hoc ANOVA test Since here the sample sizes are same, we go for the Tukey’s test for conducting the Post-hoc
ANOVA test.
In Tukey’s test, the Honestly Significant Difference (HSD) is calculated as:
5
EMST 0.434 10HSD q 3.38 0.0012
n 32
(14)
−×= = =
Where q is the student zed range statistic which is equal to a value of 3.38, for a degree of
freedom of 93 and k=3.
The difference between the individual mean values of the three iteration levels can be
summarized in a tabular form as shown below in Table 6:
Table 6 Differences of means between any two iterations
Difference Computation Numerical value
1 2µ − µ = 85.079 - 85.0735 0.0055
1 3µ − µ = 85.079 – 85.077 0.002
2 3µ − µ = 85.0735 – 85.077 -0.0035
In the above table 6, the absolute difference is of the concern and so the negative signs are to
be ignored.
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From the above Table 5, it is seen that the differences of1 2
µ − µ , 1 3µ − µ and
2 3µ − µ are all
greater than that of the HSD in equation 14, with the difference 0.00551 2
µ − µ = being the largest. So,
the differences between the means are statistically significant. Hence, it is concluded that among all
the different causes enumerated in the cause and effect diagram, the most influencing cause is the
worn out honing stones and incomplete preventive maintenance schedule. The extent of influence is
given by eta-square (η2). It measures the proportion of the between factor variability to the total
variability and is given by:
2
52
5
sum of square between the levels
sum of squares across all the 96 observations
52 100.577 57.7% 58% (15)
90 10
−
−
η =
×⇒ η = = = =
×
Hence, from above equation (15) it is deduced that 58% of variability is due to the type of the
root cause affecting Iteration 2 i.e., worn out honing stones.
6. IMPROVE AND CONTROL PHASE
In this phase the process monitoring charts are regularly employed for monitoring the crank-
pin bore diameter of the connecting rod. In addition the gauge calibration is done periodically as a
part of measurement system analysis and properly calibrated pneumatic gauge is used at the work
place. In addition to this the Design of Experiments (DOE) methodology was employed to confirm
the improvement.
6.1 Improvement through DOE The DOE had been adopted in order to improve the capability levels (sigma levels). The
initial experiments were carried out to screen out the factors that might have influenced on the
honing performance. The further experiments were used to determine the optimal settings of the
significant factors screened in initial experiments.
6.1.1 Initial Experiments In this initial experiments stage four influencing factors thought to affect the honing process
were selected. A full factorial experiment was carried out and the whole experiment was completed
in about 9 consecutive shifts, i.e., 3 days. In total about 200 components in each shift were measured
for the crank-pin bore diameter. The factors in the initial experiment are tabulated in Table 7. The
experimental conditions are as follows (1) Ambient temperature 25°C (2) Humidity 56% (3)
Machine operation no.120 (4) No. of operators :1 (5) crank-pin bore specification is 85.000 (+0.085/+0.070)
mm
Table 7 The initial experiment with levels of each factor
Factor Level 1 Level 2
Worn out honing stones at
the rear side
Before replacing the
worn-out honing stones
After replacing the worn-
out honing stones
Calibration of standard
master ring gauge Before calibration After calibration
Cleaning of air-filter of the
FRL unit Before cleaning After cleaning
Pneumatic gauge
calibration Before calibration After calibration
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From the experimental results, the main effect of the Worn out honing stones at the rear side
and cleaning of air-filter of the FRL unit showed significant influence on the crank-pin-hole
diameter. The interaction between pneumatic gauge calibration and the calibration of standard master
ring gauge were also significant. These significant effects were supported by the normal probability
plot of the standardized effects shown in Fig. 7. Since there were significant differences due to
wearing of the honing stone edge, gauge air pressure variations leading to back-pressure variation of
the pneumatic gauge, coolant recirculation pressure, hence these independent variables were taken
into consideration in the further experiments.
6.1.2 Further Experiments The further experiments were used to determine the optimal settings of the significant factors
screened.
8 5 .0 7 88 5 .0 7 68 5 .0 7 48 5 .0 7 28 5 .0 7 0
99
95
90
80
70
60
50
40
30
20
10
5
1
C1
Percent
M ean 85 .07
S tD ev 0.001917
N 32
A D 0.666
P - V a lu e 0.074
P r o b a b i l i ty P lo t o f C 1No rm a l
Fig 7 Normal Probability plot of the CTQ characteristic
In further experiments, apart from considering the previous factors (shown in Table 7) in
further experiments, wearing of the honing stone edge, gauge back-pressure variation of the
pneumatic gauge, coolant recirculation pressure were also taken into consideration. A full factorial
experiment was carried out and the whole experiment was completed in about 12 consecutive shifts,
i.e., 4 days. The levels of each factor in further experiments are given in Table 8. The experimental
conditions are as follows (1) Ambient temperature 24°C (2) Humidity 55% (3) Machine operation
no.100 (4) No. of operators :1 (5) crank-pin-hole specification is 30.000 (+0.020/0.000)
mm
Table 8 the further experiment with levels of each factor
Factor Level 1 Level 2
Worn out honing
stone working edge
Before replacing the
worn-out honing stone
After replacing the
worn-out honing stone
Gauge air-pressure
variation
Before pressure
regulation at 10kg
After pressure regulation
at 13 kg
Coolant recirculation
pressure
Coolant recirculation
pressure at 7 kg
Coolant recirculation
pressure at 12 kg
7. RESULTS AND CONCLUSION
It is seen from the design of experiments that replacement of the worn-out insert tip was the
major contributor followed by, calibration of air gage, air pressure regulation, tool presetting with v-
block and air filter maintenance followed in the said order.
As a part of standardizing the process, as per the results of ANOVA and DOE, the following
activities were carried out:
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As a part of standardizing the process, the following activities were carried out:
i. After every 5200 components being honed, the replacement of the honing stones needs to be
done.
ii. Regular machine maintenance schedule were established and regular checks were included in
the check lists.
iii. After every 10 components honed, the pneumatic gauge must be calibrated with the standard
ring gauge corresponding to the pneumatic gauging system
iv. The gauge calibration is done periodically as a part of measurement system analysis and
properly calibrated pneumatic gauge is used at the work place.
v. After every two months (about 24000 components) the FRL unit maintenance was
incorporate in the preventive maintenance checklist.
vi. Coolant recirculation pressure was set at a value of around 10 kgs.
SPC studies were found to be useful for eliminating the special cause for errors and
streamline the process and making the process to be a capable manufacturing process by improving
the Cp and Cpk values of the critical to quality characteristic under study. The cause and effect
diagram formed an important scientific tool for enlisting of the causes behind the poor performance
of the process. On adapting the DMAIC approach, the estimated standard deviation “σ” of the crank-
pin bore diameter is reduced from 0.005 to 0.002, while the process performance capability index Cpk
is enhanced from 0.34 to 1.45.
The Cp/Cpk values after performing the three iterations of data collection were greater than
1.33 and hence the process being declared as a capable process. After performing the root cause
analysis, the major root cause, confirmed by the one-way ANOVA technique, was the wearing of the
honing stones followed by improper pneumatic gage calibration. Hence, the one-way ANOVA
technique was employed successfully for identification of the root cause and its magnitude liable for
the low process capability, supported by DOE results.
REFERENCES:
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