International Journal of Mechanical Engineering and Technology

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –

6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME

116

PROCESS CAPABILITY IMPROVEMENT – A CASE STUDY OF AN

ENGINE CONNECTING ROD MANUFACTURING PROCESS

G.V.S.S.Sharma1*

, Dr.P.S.Rao2, V.Jagadeesh

3, 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

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-Analyze-

INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING

AND TECHNOLOGY (IJMET)

ISSN 0976 – 6340 (Print)

ISSN 0976 – 6359 (Online)

Volume 4, Issue 5, September - October (2013), pp. 116-129

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117

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:-

P

P K U

U S L L S LC (1 )

6

U S LC

3

−=

σ

− µ=

σ

P K L

P K

(2 )

L S LC (3 )

3

U S L L S LC m in ,

3 3

µ −=

σ

− µ µ − =

σ σ

µ = p ro c e s s m e a n , σ = s ta n d a rd d e v ia t io n

(4 )

w h e re ,

U S L a n d L S L a re 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

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 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



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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 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 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. 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 bore

110 Finish boring of crank pin bore

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 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-of-

dimension 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

characteristic, whose value is equal to 0.050100.000 ±

mm. The figure 1 below depicts the

diagrammatic view of bolt-holes center distance.

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.

Fig 2 (a)

Fig 2 (b)

Fig 2 (c)

d = Center distance between bolt holes



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4.1 Cause and Effect diagram: The critical to quality characteristic identified was the bolt-holes center distance which is

equal to 0.050100.000 ± 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



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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:



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Table 2 Measured dimensions of CTQ characteristic

S.No. Iteration 1 Iteration 2 Iteration 3 S.No. Iteration 1 Iteration 2 Iteration 3

01 99.980 100.000 100.005 17 100.01 100.010 100.020

02 99.970 99.990 99.995 18 99.980 100.000 100.005

03 99.985 100.005 99.995 19 99.955 99.970 99.995

04 100.000 100.020 100.015 20 99.990 100.010 100.015

05 99.965 99.985 100.000 21 99.960 99.980 99.995

06 99.995 100.015 100.020 22 99.980 100.000 100.005

07 99.980 100.000 100.005 23 99.995 100.015 100.020

08 99.960 99.980 100.020 24 99.965 99.985 100.005

09 100.000 100.020 100.020 25 99.995 100.015 100.020

10 99.950 99.970 100.010 26 99.990 100.010 100.015

11 99.995 100.015 100.000 27 99.955 99.975 99.995

12 99.960 99.980 100.010 28 100.000 100.020 100.020

13 99.980 100.000 100.000 29 99.960 99.980 100.005

14 100.010 100.015 100.020 30 99.990 100.010 100.015

15 99.980 100.000 100.005 31 99.965 99.985 99.995

16 99.960 99.970 100.000 32 99.990 100.010 100.015

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



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Table 3 Calculations of Cp and Cpk

Formula Iteration 1 Iteration 2 Iteration 3

USL 100.050 100.050 100.050

LSL 99.950 99.950 99.950

σ 0.017 0.016 0.009

( )6

USL LSL

CP

σ

−= 0.97 1.01 1.77

( )3

USL MEAN

CPKU

σ

−= 1.36 1.05 1.49

( )3

MEAN LSL

CPKL

σ

−= 0.57 0.97 2.06

CPK = min (CPKU , CPKL) 0.57 0.97 1.49

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.



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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 Iteration 1 Iteration 2 Iteration 3

Sample size 32 32 32

Sum 3199.350 3199.940 3200.260

Sum of squares 319870 319988 320052

Mean (µ i) 99.979 99.998 100.008

Variance (σ2) 0.00029 0.00027 0.000088

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 = 99.995

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 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.



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Hence, mean of squared deviations between levels is given by:

( )L

L

L

SST 0.01387MST = = 0.006935 (9)

df 3 1=

−

Mean of squared deviations within levels is given by:

( )E

E

E

SST 0.0204MST = = 0.000219 (10)

df 96 3

=−

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

LSTATISTIC

E

MST 0.006935F = 31.66 (11)

MST 0.000219= =

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 0.01387 0.006935 31.66

Within/error 93 0.0204 0.000219

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 equalt to, 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:

EM S T 0 .0 0 0 2 1 9

H S D q 3 .3 8 0 .0 0 8 8 4 n 3 2

(1 4 )

= = =



126

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 Computation Numerical value

1 2µ − µ = 30.002 – 30.006 -0.019

1 3µ − µ = 30.002 – 30.007 -0.029

2 3µ − µ = 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 1 2

µ − µ , 1 3µ − µ ,

2 3µ − µ 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:

2

2

sum of square between the levels

sum of squares across all the 96 observations

0.013870.4127 41.2% 41% (15)

0.0336

η =

⇒ η = = = =

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:

( )G E2

T E

2

SST k 1 MST

SST + MST

0.033819 0.3971 39.71% 40% (16)

− −ω =

⇒ ω = = = =

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 bolt-

hole 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.



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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 0.050100.000 ±

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.

1 0 0 . 0 31 0 0 . 0 21 0 0 . 0 11 0 0 . 0 09 9 . 9 99 9 . 9 8

9 9

9 5

9 0

8 0

7 0

6 0

5 0

4 0

3 0

2 0

1 0

5

1

C 1

Percent

M e a n 1 0 0 . 0

S tD e v 0 . 0 0 9 3 9 7

N 3 2

A D 1 . 3 1 0

P - V a lu e < 0 . 0 0 5

P r o b a b i l i t y P l o t o f C 1N o r m a l

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



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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 0.050100.000 ±

mm

Table 8 the further experiment with levels of each factor

Factor Level 1 Level 2

spring back effect of

the material after the

drilling operation

Low clamping force of

10kgf

Revised high clamping

force of 20 kgf

the toggle clamping

force variations

Before replacing the

toggle clamp bushes

After replacing the toggle

clamp bushes

inaccurate re-

sharpening of drill

bit and reamer

New drill bits and new

reamers

Drill bits and reamers

after resharpening

7. 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. After every 10000 components being bored, the replacement of the drill bush 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 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.



129

REFERENCES:

[1] “The transition from sampling to SPC”, by Schilling E.G.,Quality and Statistics ; Total Quality

Management, ASTM STP1209, Milton J.Kowalewski, Jr. Ed.,American Society for Testing

and Materials, Philadelphia, 1994.

[2] Locke, John W., “ Statistical Measurement Control” , Quality and Statistics: Total Quality

Management, ASTM STP 1209, Milton J. Kowalewski, Jr., Ed., American Society for Testing

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