30120130405014

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME

AND TECHNOLOGY (IJMET)

ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 4, Issue 5, September - October (2013), pp. 116-129
© IAEME: www.iaeme.com/ijmet.asp
Journal Impact Factor (2013): 5.7731 (Calculated by GISI)
www.jifactor.com

IJMET
©IAEME

PROCESS CAPABILITY IMPROVEMENT – A CASE STUDY OF AN
ENGINE CONNECTING ROD MANUFACTURING PROCESS
G.V.S.S.Sharma1*, Dr.P.S.Rao2, V.Jagadeesh3, Amit Vishwakarma4
1

Assistant Professor, Dept. of Mechanical Engg., GMR Institute of Technology, Rajam, A.P.,India.
2
Professor, Industrial Engineering Dept., GITAM University,Visakhapatnam, A.P., India.
3
Assistant Professor, Dept. of Mechanical Engg., GMR Institute of Technology, Rajam, A.P.,India.
4
Manager, Manufacturing Engineering, Volvo Eicher Commercial Vehicles Limted,
Pithampur,M.P.,India

ABSTRACT
Statistical quality control forms an excellent Quality Assurance tool for improving the quality
of manufacture and ultimately scores on the end-customer satisfaction. SQC uses process monitoring
charts for recording the critical to quality (CTQ) characteristic of the component in manufacture.
This paper elaborates on bolt-holes center distance, which forms one such CTQ characteristic of the
connecting rod manufacturing of internal combustion engine. Here the journey 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 0.017 to
0.009 and the Cp values from 0.97to 1.77 and Cpk values from 0.57 to 1.49 respectively.
Keywords: Critical to quality (CTQ) characteristic, cause and effect diagram, statistical quality
control (SQC), process monitoring charts (PMC), 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, i.e., Define-Measure-Analyze116


Improve-Control approach to improve the capability of connecting rod manufacturing process by
reducing the bolt-holes center distance variations from a nominal value.
Process mapping and identifying CTQ characteristic is carried out in “Define” phase, while estimates
of process capability indices is carried out in the “Measure phase”. One way ANOVA method of
investigation to test for the differences between the manufacturing data is employed in the “Analysis
phase”. Finally, the PMC (process monitoring chart) for the gudgeon-pin bore diameter 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 are defined as follows:USL − LSL
6σ
USL − µ
C PK U =
3σ
µ − LSL
C PK L =
3σ
 USL − µ µ − LSL 
C P K = m in 
,

3σ
3σ


w h e re ,
CP =

(1 )
(2 )
(3 )
(4 )

U S L a n d L S L a r e U p p e r a n d L o w e r s p e c if i c a t io n lim it s
µ = p r o c e s s m e a n , σ = s t a n d a r d d e v i a t io n

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

In 1994, Schilling, E.G.[1], 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-Analyse-Improve-Control (DMAIC)
approach is useful for printed circuit board quality improvement. They also proved that how DesignOf-Experiments is one of the core statistical tools for six-sigma improvement. Subsequently, MingHsien 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
117


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.
[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 sixsigma 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 methodology
for 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.
10
20
30
40
50
60
70
80
90
100
110
120
130
140

Description
Thrust face rough grinding
Gudgeon pin rough boring
Crank pin rough boring
Side face broaching
Finish grinding
Bolt hole drilling
Key way milling
Rod and cap assembly
Finish grinding of assembly
Finish boring of gudgeon pin bore
Finish boring of crank pin bore
Crank pin bore Honing
Magnetic crack detection
Final quality check, set making and dispatch to
engine assembly line.

118


3.2

Identifying CTQ characteristic
The bolt-holes center distance forms a very important dimensional characteristic as it is the
primary dimension responsible for fastening the connecting rod cap and the rod parts. Any out-ofdimension of the bolt-holes center distance of the connecting rod leads to incorrect assembly of the
cap and rod. This paves way for unequal force distribution on the rod and cap parts and subsequently
leading to engine failures and costly rework. Hence for achieving the desired engine performance the
bolt-holes center distance 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
bolt-holes center distance variations during the bolt-hole drilling and reaming operation so that these
variations are not carried to the subsequent machining operations down the manufacturing line.
The acceptable bolt-holes center distance tolerance zone variation for was limited to 1.000 mm. The
connecting rods which were out of these tolerance limits resulted in inaccurate assembly of the cap
and rod parts and subsequently got rejected. Even costly repair and rework could not restore the
dimension back. Hence bolt-holes center distance was of the main concern and identified as a CTQ
± 0.050
mm. The figure 1 below depicts the
characteristic, whose value is equal to 100.000
diagrammatic view of bolt-holes center distance.

Fig 2 (a)

Fig 2 (b)
d = Center distance between bolt holes

Fig 2 (c)

Fig. 2 (a): Center distance ‘d’ of bolt holes shown on the connecting rod assembly view. Fig
2 (b): Center distance ‘d’ of bolt holes shown on the rod. Fig 2 (c): Center distance ‘d’ of bolt holes
shown on the cap.
4.

MEASUREMENT PHASE

In this phase the data of bolt-holes center distance on cap and rod subassemblies for 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 bolt-holes center distance 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.

119


4.1

Cause and Effect diagram:
The critical to quality characteristic identified was the bolt-holes center distance which is
equal to 100.000 ± 0.050 mm whose machining tolerance zone is equal to 1.000 mm. The Cp value, i.e.,
the process potential capability index, {Cp= (USL-LSL)/6σ}, nominally was equal to 0.97, which
was below the acceptance level limit of greater than 1.33 for the above CTQ characteristic. 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, also known as the Fish-bone
diagram or Ishikawa diagram [2][6], was employed. , as show below in Fig3

Fig3: Cause and Effect diagram showing the variables affecting the bolt-holes center distance during
the engine connecting rod bolt hole drilling operation
One important aspect to be mention at this juncture is the description of bolt-hole drilling and
reaming operation set up. The machine employed was horizontal axis drilling machine of MICO
make. It consists of a gang drilling attachment with dual drill set up where both the drills act
simultaneously through the jig bushes. The machine platform was equipped with limit switch gauges
for accurate positioning of the component locating fixtures onto the gang drill bits. Below shown is
the machine snap shot of the horizontal axis bolt hole drilling and reaming machine employed for
bolt hole drilling and reaming of con-rod.

Fig 5: Horizontal axis bolt hole drilling machine

120


4.2

Process FMEA (failure modes and effects analysis)

FMEA sheet for bolt hole drilling is shown in following Fig 4. 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.2. It can be noticed that the highest RPN (Risk
Prority Number) is for the potential failure “bolt hole center distance more” and followed by the
potential failure “Bolt hole center distance less”. Hence the bolt-hole center distance variations are of
the prime concern and hence are liable for improvement action. The data collection was performed
and the measurements of the bolt-hole center distance variations were recorded for further analysis
and improvement of process capability.

Fig 4 FMEA sheet
4.3

Data collection:
Data collection of the critical to quality characteristic was performed for 32 consecutive
machined cap and rod components. Data collection was performed in 3 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:

121


Table 2 Measured dimensions of CTQ characteristic
S.No.

Iteration 1

Iteration 2

Iteration 3

S.No.

Iteration 1

Iteration 2

Iteration 3

01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16

99.980
99.970
99.985
100.000
99.965
99.995
99.980
99.960
100.000
99.950
99.995
99.960
99.980
100.010
99.980
99.960

100.000
99.990
100.005
100.020
99.985
100.015
100.000
99.980
100.020
99.970
100.015
99.980
100.000
100.015
100.000
99.970

100.005
99.995
99.995
100.015
100.000
100.020
100.005
100.020
100.020
100.010
100.000
100.010
100.000
100.020
100.005
100.000

17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32

100.01
99.980
99.955
99.990
99.960
99.980
99.995
99.965
99.995
99.990
99.955
100.000
99.960
99.990
99.965
99.990

100.010
100.000
99.970
100.010
99.980
100.000
100.015
99.985
100.015
100.010
99.975
100.020
99.980
100.010
99.985
100.010

100.020
100.005
99.995
100.015
99.995
100.005
100.020
100.005
100.020
100.015
99.995
100.020
100.005
100.015
99.995
100.015

The data in the 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 Fig 6.
5.

ANALYSIS PHASE

The analysis phase comprises of performing the calculations for the Cp and Cpk values across
each iteration. And testing the differences between the three iterations using one way ANOVA
method.

Fig 6 Process Monitoring chart
5.1
Calculations of Cp and Cpk
The calculations of Cp and Cpk are tabulated as below in Table 3

122


Table 3 Calculations of Cp and Cpk
Formula

CP =

(USL − LSL )

100.050
99.950

100.050
99.950

0.016

0.009

0.97

σ

Iteration 3

0.017

LSL

Iteration 2

100.050
99.950

USL

Iteration 1

1.01

1.77

1.36

1.05

1.49

0.57

0.97

2.06

0.57

0.97

1.49

6σ

C PKU =

(USL − MEAN )
3σ

C PKL =

( MEAN − LSL )
3σ

CPK = min (CPKU , CPKL)

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 bolt hole center distances of both the connecting rod and cap
after the bolt hole drilling and reaming operations (operation no.60) were captured with the help of a
venire caliper set up.
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 0.97 and 0.57, which
Is less than that for process to be capable, i.e., 1.33. Hence, next set of data is captured after
replacing the worn out jig bushes and tool, jig and fixture maintenance.
5.1.2 Iteration no. 2
In this Iteration, data is collected after the machine preventive maintenance schedule
completion and replacement of worn out jig bushes.
From the above set of data from Table 3 it is seen that there is a marginal increase of Cp from 0.97 to
1.01 and Cpk from 0.57 to 0.97. 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 performing the regular maintencance work for
proximity limit switches.
From the above set of data from Table 3 it is seen that there is a noticeable increase of Cp
from 0.97 to 1.77 and Cpk from 0.94 to 1.49. Now. Since both Cp and Cpk are greater than 1.33
hence the bolt hole drilling and reaming machining process is declared as a capable process.
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 and also to determine the extent of influence of the causes
responsible for low process capability.
123


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.
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, means and variance
for each iteration is tabulated in the table 4 below:Table 4: Mean and variance of all the three iterations
Formula
Sample size
Sum
Sum of squares
Mean (µ i)
Variance (σ2)

Iteration 1
32
3199.350
319870
99.979
0.00029

Iteration 2
32
3199.940
319988
99.998
0.00027

Iteration 3
32
3200.260
320052
100.008
0.000088

If xij denote the data from the ith level and jth observation, then overall or grand mean is given by :4 32 x
ij
µ = ∑∑ ,
(5)
i =1 j=1 N
Where N is the total sample size of all the three iterations i.e., 32x3=96
Hence, from equation (5), we get, µ = 99.995
The sum of squared deviations about the grand mean across all N observations is given by:
4

32

SSTT = ∑∑ ( x ij − µ )

2

(6)

i =1 j=1

The sum of squared deviations for each level mean about the grand mean is given by:
4

SSTL = ∑ 4 × ( µi − µ )

2

(7)

i =1

The sum of squared deviations for all observations within each level from that level mean, summed
across all levels is given by :4

32

SSTE = ∑∑ ( x ij − µi )

2

(8)

i =1 j=1

From equations (6), (7) and (8), the values of SSTT, SSTG and SSTE obtained are 0.0336,
0.01387 and 0.0204 respectively.
On dividing SSTT, SSTL and SSTE by their associated degrees of freedom (df), we get mean
of squared deviations respectively.
124


Hence, mean of squared deviations between levels is given by:
SSTL 0.01387
=
= 0.006935
df L
( 3 − 1)
Mean of squared deviations within levels is given by:
MSTL =

MSTE =

SSTE 0.0204
=
= 0.000219
df E
( 96 − 3)

(9)

(10)

Finally, the F Statistic is given by the following formula:
FSTATISTIC =

MSTL 0.006935
=
= 31.66
MSTE 0.000219

(11)

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
Level
Within/error
Total

df
2
93
95

Sum of
squares
0.01387
0.0204
90x10-5

Mean of
squares
0.006935
0.000219

F
31.66

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 equalt to, F CRITICAL (α, K-1, N-K). and
is obtained to be 3.094. Thus,
FCRITICAL = 3.094

(12)

From equations (11) and (12) it is seen that:
FSTATISTIC > FCRITICAL

(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:

HSD = q

M S TE
= 3 .3 8
n

125

0 .0 0 0 2 1 9
= 0 .0 0 8 8 4
32
(1 4 )


Where q is the studentized 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

µ1 − µ 2 =

Computation
30.002 – 30.006

Numerical value
-0.019

µ1 − µ3 =

30.002 – 30.007

-0.029

µ 2 − µ3 =

30.006 – 30.007

-0.010

In the above table 6, the absolute difference is of the concern and so the negative signs are to
be ignored.
From the above Table 5, it is seen that the differences of µ1 − µ 2 , µ1 − µ3 , µ 2 − µ3 are greater
than that of the HSD in equation 14. 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 causes are the worn out drill jig bushes and non-maintencance
of the proximity limit switch leading to hunting of the measurements readings. 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:
sum of square between the levels
η2 =
sum of squares across all the 96 observations
0.01387
⇒ η2 =
= 0.4127 = 41.2% = 41% (15)
0.0336
Eta-square is just a ratio of treatment effect variability to total variability. One drawback with
eta-square is that it is a biased estimate and tends to overestimate the effect. A more accurate
measure of the effect is the omega-square (ω2) given by:
ω2 =

SSTG − ( k − 1) MSTE
SSTT + MSTE

⇒ ω2 = 0.033819 = 0.3971 = 39.71% = 40% (16)
Hence, from above equation (16) it is deduced that 40% of variability is due to the worn out
drill jig bushes and non-maintenance of the proximity limit switch.

6.

IMPROVE AND CONTROL PHASE

In this phase the process monitoring charts are regularly employed for monitoring the bolthole center distance of the connecting rod. In addition to this the Design of Experiments (DOE)
methodology was employed to improve the process capability.
6.1

DOE procedure
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
drilling performance. The further experiments were used to determine the optimal settings of the
significant factors screened in initial experiments.
126


6.1.1 Initial Experiments
In this initial experiments stage four influencing factors thought to affect the boring 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 bolt-hole center distance. 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.60 (4) No. of operators :1 (5) bolt-holes center distance is equal to
100.000 ± 0.050 mm
Table 7 the initial experiment with levels of each factor
Factor

Level 1

Level 2

Worn out drill bush

Before replacing the drill bush

After replacing the drill bush

Calibration of venire
caliper

Before calibration

After calibration

limit switch malfunction

Before repairing the limit
switch

After repairing the limit switch

Set-up changeover
inaccurate

Manual drill bit loading

Drill bit loading with a portable
v-block insert setting gauge

From the experimental results, the main effect of the proximity limit switch malfunction and
calibration of venire caliper showed significant influence on the bolt-holes center distance. The
interaction between worn out drill bush and the accuracy of set-up changeover 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 spring back effect of the material
after the drilling operation, the toggle clamping force variations, inaccurate re-sharpening of drill bit
and reamer, hence these independent variables were taken into consideration in the further
experiments.
P r o b a b ility P lo t o f C 1
N o rm a l
99
M ean
S tD e v
N
A D
P - V a lu e

95
90

1 0 0 .0
0 .0 0 9 3 9 7
32
1 .3 1 0
< 0 .0 0 5

80

Percent

70
60
50
40
30
20
10
5

1

9 9 .9 8

9 9 .9 9

1 0 0 .0 0

1 0 0 .0 1

1 0 0 .0 2

1 0 0 .0 3

C1

Fig 7 Normal Probability plot of the CTQ characteristic
6.1.2 Further Experiments
The further experiments were used to determine the optimal settings of the significant factors
screened.
In further experiments, apart from considering the previous factors (shown in Table 7) in
further experiments, spring back effect of the material after the drilling operation, the toggle
clamping force variations, inaccurate re-sharpening of drill bit and reamer 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
127


in Table 8. The experimental conditions are as follows (1) Ambient temperature 24°C (2) Humidity
55% (3) Machine operation no.60 (4) No. of operators :1 (5) bolt-holes center distance is equal to
100.000 ± 0.050 mm
Table 8 the further experiment with levels of each factor
Factor
spring back effect of
the material after the
drilling operation
the toggle clamping
force variations
inaccurate resharpening of drill
bit and reamer

7.

Level 1

Level 2

Low clamping force of
10kgf

Revised high clamping
force of 20 kgf

Before replacing the
toggle clamp bushes

After replacing the toggle
clamp bushes

New drill bits and new
reamers

Drill bits and reamers
after resharpening

RESULTS AND CONCLUSION

It is seen from the design of experiments that proximity limit switch accuracy was the major
contributor followed by, calibration venire caliper, worn out drill bush, accuracy of set-up
changeover, and spring back effect of material after drilling operation, toggle-clamping force
variations and accuracy of resharpened drill-bits, 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:
i.
ii.

After every 10000 components being bored, the replacement of the drill bush needs to be done.
Regular machine maintenance schedule were established and regular checks were included in the
check lists.
iii. After every 10 components bored, the accuracy of the limit switch was tested.
iv.
The venire caliper calibration is done periodically as a part of measurement system analysis.
v.
After every one month (about 12000 components) the drill-bit needs to be resharpened.
vi.
Coolant recirculation pressure was set at a value of around 10 kgs.
vii.
Drill-bit presetting on the drill tool holder was carried out by the help of a portable v-block insert
setting gauge on the horizontal drill mandrel.
viii. Component clamping force was raised to 20 kgf from 10 kgf in order to avoid any spring back
effects of the component material after the drilling and reaming operations.
SPC studies were found to be useful for eliminating the special cause for errors and
streamling 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 0.017 to 0.009, while the process performance capability index Cpk is
enhanced from 0.57 to 1.49.
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 cutting tool
inserts followed by improper pneumatic gage calibrations. Hence, the one-way ANOVA technique
was employed successfully for identification of the root cause and its magnitude liable for the low
process capability. This was further strengthened by the design of experiments results.

128


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