Predicting physico-mechanical properties of LLDPE and PDMS rubber blends using ANFIS-based model

Abstract

Blends of linear low density polyethylene (LLDPE) and poly dimethyl siloxane (PDMS) rubber have been identified as a promising material for cable insulation. The influence of processing parameters on the physico-mechanical properties of these blends has been investigated and the same has been optimised for preparing these blends. In practice, it is not possible to carry out several experiments to identify the relationship between the intermediate processing parameters and physical properties. To address this issue, the relationship between the processing parameters and the mechanical properties of the optimised LLDPE and PDMS rubber blends have been mapped using a non-linear system identification technique namely, adaptive-network-based fuzzy inference system (ANFIS) without carrying further experiments, but with the available experimental data. In this model, the effect of the number of fuzzy rules on the model performance has been investigated to predict the properties and to correlate the above relationship. This paper reports the development of ANFIS-based model for predicting the LLDPE/PDMS rubber blends in reducing the experimental trials and its success over the ANN model.

Keywords

ANFIS NEURO-FUZZY POLYMER BLENDS PHASE MORPHOLOGY MECHANICAL PROPERTIES THERMOPLASTIC ELASTOMERS

References

Kenzle

S. Y.

: ‘Polymer blends and alloys: a guide book to commercial products’, 1-11; 1988, Lancaster, Technomic Publishing Co.

Falender

J. L.

, Lindsey

S. E.

and Saam

J. C.

: Polym. Eng. Sci , 1976, 16, 54–58.

Santra

R. N.

, Samantaray

B. K.

, Bhowmick

A. K.

and Nando

G. B.

: J. App. Polym. Set , 1993, 49, 1145–1158.

Jana

R. N.

, Nando

G. B.

and Khastgir

: Plast. Rubber Compos ., 2003, 32, 11–20.

Jalali-Arani

, Katbab

A. A.

and Nazockdast

: J. AppL Polym. Sci , 2003, 90, 3402–3408.

Jalali-Arani

, Katbab

A. A.

and Nazockdast

: J. AppL Polym. Sci , 2005, 96, 155–161.

Sureshkumar

M. S.

, Naskar

, Nando

G. B.

, Bhardwaj

Y. K.

and Sabharwal

: Polym. Plast. Technot Eng ., 2007, 47, 341–345.

Joseph

and Thomas

: Eur. Polym. J , 2003, 39, 115–125.

: Polym. Eng. Set , 1982, 27, 335–343.

10.

Utracki

L. A.

and Shi

Z. H.

: Polym. Eng. Sci, 1992, 32, 1824-1833.

11.

Sundararaj

and Macosko

C. W.

: Macromolecules , 1995, 28, 2647–2657.

12.

R. M.

, Wu

C. H.

and Su

A. C.

: Polym. Eng. Sci , 1990, 30, 511–518.

13.

Rovatti

and Bobalek

E. G.

: J. Appl. Polym. Sci , 1963, 7, 2269–2292.

14.

Avgeropoulos

G. N.

, Weissert

, Biddison

and Bhm

G. G. A.

: Rubber Chem. Technot , 1976, 49, 93–104.

15.

Vanoene

: J. Colloid Interf Set , 1972, 40, 448–467.

16.

Mourada

A. H. I.

, Bekheetb

, El-Butchc

, Abdel-Latifc

, Nafeec

and Bartond

D. C.

: Polym. Test ., 2005, 24, 169–180.

17.

Sahin

and Yayla

: Polym. Test ., 2005, 24, 1012–1021.

18.

Sureshkumar

M. S.

, Naskar

and Nando

G. B.

: Proc. India Int. Rubber Conf. (IIRC), Udaipur, India, November 2007, Mohan Lal Sukhadia University, Udaipur, India, Paper 4D.

19.

Sengupta

: ‘Morphology of olefinic thermoplastic elastomer blends’, PhD thesis, University of Twente, Twente, The Netherlands, 2004.

20.

Case

F. H.

: J. Comput.-Aided Mater. Des ., 1996, 3, 369–378.

21.

Hussain

: Artif. IntelL Eng. Des ., 1999, 13, 55–68.

22.

Ganguly

, Sadhukhan

and Saraf

D. N.

: Ind Chem. Eng. A , 2002, 44A, 294–299.

23.

Zhang

and Friedrich

: Compos. Set TechnoL , 2003, 63, 2029–2044.

24.

Chitanov

, Kiparissides

and Petrov

: Proc. 2nd IEEE Int. Conf. on ‘Intelligent systems’, Varna, Bulgaria, June 2004, IEEE, 67-72.

25.

Jang

J. S. R.

: IEEE Trans. Syst. Man Cybern ., 1993, 23, 665–685.

26.

Khalifehzadeh

, Forouzan

, Arami

and Sadrnezhaad

S. K.

: Comput. Mater. Sci, 2007, 40, 359–365.

27.

S. P.

: J. Mater. Process. Technot , 2003, 142, 665–675.

28.

Y. H.

, Yeh

F. H.

, Li

C. L.

, Wu

M. T.

and Liu

C. L.

: J. Mater. Process. Technot , 2003, 140, 576–582.

29.

Nariman-Zadeh

, Darvizeh

and Dadfarmai

M. H.

: J. Mater. Process. Technot , 2004, 155–156, 1415-1421.

30.

Suresh

: ‘Real time inferencing of polymer properties neuro-fuzzy and wavenet-fuzzy based models’, MSc thesis, Indian Institute of Technology, Kharagpur, India, 2005.

31.

Yeh

F. H.

, Lu

Y. H.

, Li

C. L.

and Wu

M. T.

: J. Mater. Process. TechnoL , 2006, 173, 136–144.

32.

Tsai

K. M.

and Wang

P. J.

: J. Mater. Process. Technot , 2001, 117, 111–124.

33.

Inal

: J. Mater. Process. Technol , 2008, 195, 34–43.

34.

Jang

J. S. R.

and Mizutani

: Proc. Biennial Conf. of the North American Fuzzy Information Processing Society, Berkeley, CA, USA, June 1996, IEEE, 87-91.

35.

Yen

and Langari

: ‘Fuzzy logic: intelligence, control and infor-mation’, 1st Indian reprint, 64; 2003, Delhi, Pearson Education.