World Journal Of Engineering
Prediction of Mechanical and Barrier Properties of Chlorobutyl Rubber/Natural Rubber
Blend using Artificial Neural Networks
Lin Li, Jin Zhang, Z X Zhang, Jin Kuk Kim*
School of Nano & Advanced Materials Engineering, Gyeongsang National University,
660-701, South Korea
Three important factors identified to have
simulation. Fig.1a shows the contour plots
maximum impact on the properties of the
of the effect of CB and the CIIR/NR ratio
blends are the CIIR/NR ratio, oil and carbon
contents at constant of oil (10 phr) on break
black contents. Three important levels are
elongation. Fig.1b shows the contour plots
identified to quantitate every factor
of the effect of oil and the CIIR/NR ratio
respectively. The three levels for the
contents at constant of CB (55 phr) on break
CIIR/NR ratio are 90/10, 80/20 and 70/30
elongation. We can find that break
(The total weight of the blend is kept at
elongation is maximum in the region
100phr). Carbon black is varied in three
10.50phr≤oil≤14.33phr, 7.12≤the CIIR/NR
levels (40phr, 55phr and 70phr), and oil is
ratio ≤8.78, 58.86phr≤CB≤68.5phr.
varied in three levels (5phr, 10phr and
15phr). Although there are three factors and
three levels involved for every factor, nine
experimental runs are taken by orthogonal
experimental design (L9(34))[1-2].
Mechanical properties
a
a
b
Fig. 2 Contour plots of the effect of the
CIIR/NR ratio, CB and oil on ultimate
tensile strength
Fig.2a shows the contour plots of the effect
of oil and the CIIR/NR ratio contents at
constant of CB (55 phr) on ultimate tensile
strength. Fig.2b shows the contour plots of
the effect of oil and CB contents at constant
of the CIIR/NR ratio (5.65) on ultimate
tensile strength. We can find that ultimate
tensile strength is maximum in the region
5.95phr≤oil≤9.10phr,2.30≤theCIIR/NRratio
≤7.12,56.00phr≤CB≤70.00phr.
b
Fig.1 Contour plots of the effect of the
CIIR/NR ratio, CB and oil on break
elongation
The
RCAD
program
gave
two-dimensional contour plots after the
663
World Journal Of Engineering
By examining the overall performance
of each sample, the prediction of the
optimum formula combination by RCAD is
that rubber ratio of CIIR/NR is 87.50/12.50,
the optimum concentrations of carbon black
and oil are 66.25phr and 10.25phr for
CIIR/NR compound, respectively.
Table1 Comparison of experimental
properties and those predicted by RCAD
Predicted by Experimental
properties
RCAD
value
Tensile
4.62Mpa
4.55Mpa
Strength
Break
492.21%
481.87%
Elongation
Permeability 3.59
3.64
Table 1 shows the comparison of properties
between the predicted and the experimental
values. It is observed that the predicted and
the experimentally determined properties are
fairly close to each other and thereby
confirming that orthogonal experimental
design-based
ANN–GA
optimization
procedure can be used for the prediction of
properties with a fair degree of accuracy.
Barrier property
a
b
Fig.3 Contour plots of the effect of the
CIIR/NR ratio, CB and oil on permeability
Fig.3a shows the contour plots of the
effect of oil and the CIIR/NR ratio contents
on permeability (where CB content is
constant at 55 phr). It additionally shows
that at a fixed ratio of CB content oil has
little effect on the permeability property
compared to the CIIR/NR ratio. Fig.3b
shows the contour plots of the effect of oil
and CB contents on permeability (where the
CIIR/NR ratio is constant at 9.0). It
additionally shows that at a fixed the
CIIR/NR ratio oil has little effect on the
permeability property compared to CB level.
So the result indicates that the CIIR/NR
ratio and CB level are the important factors
rather than the oil level in effecting on
permeability of blends. We can find that
permeability is maximum in the region
6.60≤the
CIIR/NR
ratio
≤9.00,
59.20phr≤CB≤70.00phr,5phr ≤oil≤10.60phr.
Conclusion
The predicted optimum formulation was
found to be parallel to the experimental
recipe, thereby validating the accuracy of
the orthogonal design method-based
ANN–GA
optimization
procedure.
Reference
[1]White, H. Artificial neural networks:
approximation
and
learning
theory.
NewYork: Blackwell (1992).
[2] Cook, D.F., Ragsdale, C.T., Major, R.L.
Combining a neural network with a genetic
algorithm
for
process
parameter
optimization. Eng Appl Artif Intell, 13(2000),
391–396.
664