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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.Sharma 1* , Dr.P.S.Rao 2 , V.Jagadeesh 3 , and Amit Vishwakarma 4 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 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 C p and C pk 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 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, pp. 84-97 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2013): 5.8376 (Calculated by GISI) www.jifactor.com IJARET © I A E M E

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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

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, pp. 84-97 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2013): 5.8376 (Calculated by GISI) www.jifactor.com

IJARET

© I A E M E

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6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 6, September – October (2013), © IAEME

85

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:

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

96

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.

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