An industrial rubber manufacturer was experiencing 20% rejection rates in its calendering process due to thickness variation in the rubber sheets; analyzing multivariable charts revealed that thickness dropped when input rubber rolls neared completion, so switching to smaller 1kg rolls with improved insulation between processing reduced thickness variation and rejections to under 2% within 4 days.

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Chronic Problem?
Solve it now !

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Here is a case study.
One Industrial Rubber Parts Manufacturer
had a problem in his calendering process.
The critical process in the business
was calendering,
in which rubber mix was passed
between 2 rollers
to get sheets of desired thicknesses.
The sheets were later vulcanised.

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Background
Current Situation & Target
The process produced 20 %
Rejection. Rejected material was
recycled.
This caused further damage in
terms of deteriorated batches.
•Target = 0 %
1. This was stated to be a Chronic problem
2. The calendering machine was very old awaiting
replacement. There was delay in ordering new
machine due to cash flow problems.
3. They knew the problem and were Living with it.
4. There was apparently no known solution to the
problem except machine replacement.

4. Page 4www.sigmaguru.com
0.55
0.540.54
0.58
0.600.60
0.65
0.61
0.57
0.58
0.55
0.50
0.52
0.550.550.55
0.54
0.57
0.50
0.48
0.560.56
0.57
0.60
0.61
0.50
0.52
0.55
0.56
0.58
0.56
0.58
0.600.60
0.58
0.59
0.62
0.65
0.570.570.57
0.48
0.50
0.56
0.58
0.55
0.58
0.60
0.51
0.50
0.580.58
0.60
0.62
0.64
0.52
0.53
0.57
0.56
0.59
0.45
0.50
0.55
0.60
0.65
0.70
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SAMPLES
MM
Cp=0.25
Shainin recommends use of multivari if you have no clue to the
possible solution.
Convert available data into various multivaris.
Here is one in the form of run chart ,
Showing thickness variation in a 40 kg batch in current production.
It uses 4 rolls of 10 kg each/ with thickness of 10mm, as input .

5. Page 5www.sigmaguru.com
0.55
0.540.54
0.58
0.600.60
0.65
0.61
0.57
0.58
0.55
0.50
0.52
0.550.550.55
0.54
0.57
0.50
0.48
0.560.56
0.57
0.60
0.61
0.50
0.52
0.55
0.56
0.58
0.56
0.58
0.600.60
0.58
0.59
0.62
0.65
0.570.570.57
0.48
0.50
0.56
0.58
0.55
0.58
0.60
0.51
0.50
0.580.58
0.60
0.62
0.64
0.52
0.53
0.57
0.56
0.59
0.45
0.50
0.55
0.60
0.65
0.70
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
SAMPLES
MM
Cp=0.25
See strong relationship
between input and output sheet thickness variation
Everytime a new roll is fed, thickness goes up
See 4 ups and downs
40 kg batch requires 4 rolls of 10kg as input
This clue was studies further
First roll of 10 kg Fourth rollSecond roll Third roll

6. Page 6www.sigmaguru.com
0.55
0.54 0.54
0.58
0.60 0.60
0.65
0.61
0.57
0.58
0.55
0.50
0.52
0.55 0.55 0.55
0.54
0.57
0.50
0.48
0.56 0.56
0.57
0.60
0.61
0.50
0.52
0.55
0.56
0.58
0.56
0.58
0.60 0.60
0.58
0.59
0.62
0.65
0.57 0.57 0.57
0.48
0.50
0.56
0.58
0.55
0.58
0.60
0.51
0.50
0.58 0.58
0.60
0.62
0.64
0.52
0.53
0.57
0.56
0.59
0.45
0.50
0.55
0.60
0.65
0.70
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
S AMPLES
Cp=0.25
Look at this multivari very closely
As input roll is getting finished, thickness drops to a low value
Once a new roll is fed, thickness goes up quickly
see this happening 3 times
40 kg batch requires 4 rolls of 10kg as input
You miss this clue….. And you lose a possible solution.

7. Page 7www.sigmaguru.com
Scatter diagram
7
Sigma AS % of average
Input rolls
unit weight in kgs END-1 END-2 AVERG
15 4.59 5.38 5.0
10 6.78 7.09 6.9
5 3.62 3.85 3.7
2.5 3.92 2.83 3.4
1 2.42 2.71 2.6
Multivari - sigma AS % vs feed weight
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
15 KG 10 KG 5 KG 2.5 KG 1 KG
KG FEED
AS%
current
new

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Confirmation of results
Thickness variation with 1 KG rolls/ 7.5 MM THICK
0.5
0.51
0.52
0.53
0.54
0.55
0.56
0.57
0.58
0.59
0.6
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SAMPLE
MM
Cp=0.66

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Initial Deviation is during set up
Deviation at the end of roll is due to reduced input roll
temp resulting in hardening.
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SAMPLE
MM
END-1
END-2
MINSTD
MAXSTD
Cp=0.66

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By improving insulation while waiting to be processed, roll
temp reduction was reduced and new process looked like
this.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
SAMPLE
MM
END-1
END-2
MINSTD
MAXSTD
Cp=0.83

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The improved process brought down rejection
under 2 %
from an initial high of 20%.
Time taken - 4 days
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SAMPLE

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Conclusion
DOE can solve the toughest Quality problems,
In a very short time,
Without expensive inputs.

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Work pending
Further application of DOE
will being this rejection down to
near zero.
And correspondingly higher Cp
level.

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Do you have a process crying for improvement?
Get in touch with
sigmaguru at gmail.com
or
Vidyut Bapat at +91 94235 07631