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

  • C. Lotti and R. E. S. Bretas
Published/Copyright: March 26, 2013
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International Polymer Processing
From the journal International Polymer Processing Volume 22 Issue 1

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:

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

© 2007, Carl Hanser Verlag, Munich

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  6. Coating and Polymer Processing
  7. Practical Limitations to Carrier Layer Formation on Inclined Planes
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  13. Review Article
  14. Welding of Plastics: Fundamentals and New Developments
  15. Regular Contributed Articles
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  19. Regular Contributed Articles
  20. Residual Wall Thickness Distribution at the Transition and Curve Sections of Water-assisted Injection Molded Tubes
  21. Numerical Simulation of Extrusion Coating
  22. Correlation between Injection Molding Parameters, Morphology and Mechanical Properties of PPS/SEBS Blend Using Artificial Neural Networks
  23. PPS News
  24. PPS News
  25. Seikei-Kakou Abstracts
  26. Seikei-Kakou Abstracts
https://doi.org/10.3139/217.0991

Articles in the same Issue

  1. Contents
  2. Contents
  3. Editorial
  4. International Polymer Processing Special Issue: European Coating Symposium 2005, University of Bradford, UK
  5. Invited Papers
  6. Coating and Polymer Processing
  7. Practical Limitations to Carrier Layer Formation on Inclined Planes
  8. Simulation of 3D Crystallization of Colloidal Nanoparticles on a Substrate during Drying
  9. Asymmetric Surface Roughness Formationon Moving Non-isothermal Liquid Coatings
  10. Inkjet Printing of Conductive and Resistive Coatings
  11. Moisture Sorption and Transport in Polylactide
  12. 10.3139/217.0992
  13. Review Article
  14. Welding of Plastics: Fundamentals and New Developments
  15. Regular Contributed Articles
  16. Numerical Simulation of Viscous Flow in a Partially filled Co-rotating Twin Screw Extruder
  17. Invited Papers
  18. A New Calculation Model and Optimization Method for Maddock Mixers in Single Screw Plasticising Technology
  19. Regular Contributed Articles
  20. Residual Wall Thickness Distribution at the Transition and Curve Sections of Water-assisted Injection Molded Tubes
  21. Numerical Simulation of Extrusion Coating
  22. Correlation between Injection Molding Parameters, Morphology and Mechanical Properties of PPS/SEBS Blend Using Artificial Neural Networks
  23. PPS News
  24. PPS News
  25. Seikei-Kakou Abstracts
  26. Seikei-Kakou Abstracts
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