Startseite Correlation between Injection Molding Parameters, Morphology and Mechanical Properties of PPS/SEBS Blend Using Artificial Neural Networks
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Correlation between Injection Molding Parameters, Morphology and Mechanical Properties of PPS/SEBS Blend Using Artificial Neural Networks

  • C. Lotti und R. E. S. Bretas
Veröffentlicht/Copyright: 26. März 2013
Veröffentlichen auch Sie bei De Gruyter Brill

Abstract

The objectives of this work were to identify the injection molding processing variables with the greatest effect on the morphology and mechanical properties of an injection molded blend made of poly (p-phenylene sulphide), PPS and block copolymer styrene-ethylene-butadiene-styrene ‘SEBS’. Artificial Neural Networks, ANNs, are used as an alternative method to constitutive and empirical models, to predict morphological features and mechanical properties from the injection molding conditions, and to predict mechanical properties from the morphological features.

The quantification of SEBS dispersion in the PPS matrix was done using a dispersion function. Mold temperature and flow rate were the processing variables with the highest influence on the entire morphology, while the holding pressure influenced mainly the inner layers. Impact strength and toughness were most influenced by mold temperature, holding pressure and the outer layers. The flexural modulus was influenced by all processing variables and the intermediate layers.

Three different ANNs were evaluated: one (ANN-1) to predict morphology from processing conditions and another two to predict mechanical properties from morphology and from processing conditions (ANN-2 and ANN-3, respectively). These latter ANN models had similar results, indicating that both inputs could be successfully used to predict mechanical properties, as the mean residuals were close to experimental errors. On the other hand, ANN-1 showed a lower performance, with a mean error smaller than the experimental error, suggesting that ANNs could overtake some inherent uncertainties.

In this case, it was concluded that the distribution of data along output domain was more important than a high number of training data in the ANN's performance.


Mail address: R. E. S. Bretas, Department of Materials Engineering, Universidade Federal de São Carlos, 13565-905 São Carlos, SP, Brasil. E-mail:

References

1Deyrail, Y., Fulchiron, R., Cassagnau, P.: Polym. 43, p. 3311 (2002)10.1016/S0032-3861(02)00134-9Suche in Google Scholar

2Osswald, T. A., Menges, G.: Materials Science of Polymers for Engineers. Hanser Publishers, Munich, Vienna, New York (1995)Suche in Google Scholar

3Son, Y., Ahn, K. N., Char, K.: Polym. Eng. Sci. 40, p. 1376 (2000)10.1002/pen.11267Suche in Google Scholar

4Son, Y., Ahn, K. N., Char, K.: Polym. Eng. Sci. 40, p. 1385 (2000)10.1002/pen.11268Suche in Google Scholar

5Hage, E., Hale, W., Keskkula, H., Paul, D. R.: Polym. 38, p. 3237 (1997)10.1016/S0032-3861(96)00879-8Suche in Google Scholar

6Li, Z., Narh, K. A.: Compos. Part B32, p. 103 (2001)10.1016/S1359-8368(00)00046-9Suche in Google Scholar

7Hay, J. N., Luck, D. A.: Polym. 42, p. 8297 (2001)10.1016/S0032-3861(01)00335-4Suche in Google Scholar

8Lotti, C., Bretas, R. E. S.: Intern. Polym. Process. 2, p. 104 (2006)Suche in Google Scholar

9Moldflow user's manual, v. 4. 1 (2004)Suche in Google Scholar

10Bretas, R. E. S., Colias, D., Baird, D. G.: Polym. Eng. Sci. 34, p. 1492 (1994).10.1002/pen.760341909Suche in Google Scholar

11Ito, E. N., Pessan, L. A., Covas, J. A., HageJr., E.: Intern. Polym. Process. 4, p. 376 (2003)10.3139/217.1780Suche in Google Scholar

12ScobboJr., J. J., in: Polymer blends: Performance. Paul, D. R., Bucknall, C. B. (Eds.), John Wiley & Sons, New York, Chichester, Weinhein, Brisbane, Singapore, Toronto (2000)Suche in Google Scholar

13SNNS – “Stuttgart Neural Network Simulator”, User's guide, version 4.2, University of Tübingen, Denmark, www-ra.informatik.uni-tuebingen.de/SNNSSuche in Google Scholar

14Twomwy, J. M., Smith, A. E., in: Artificial Neural Networks for Civil Engineers: Fundamentals and Applications, ASCE Press (1996)Suche in Google Scholar

Received: 2006-6-7
Accepted: 2006-8-10
Published Online: 2013-03-26
Published in Print: 2007-03-01

© 2007, Carl Hanser Verlag, Munich

Heruntergeladen am 26.11.2025 von https://www.degruyterbrill.com/document/doi/10.3139/217.0991/html?lang=de
Button zum nach oben scrollen