Correlation between injection molding parameters, morphology and mechanical properties of PPS/SEBS blend using artificial neural networks / C. Lotti in INTERNATIONAL POLYMER PROCESSING, Vol. XXII, N° 1 (03/2007)  

[article]  inINTERNATIONAL POLYMER PROCESSING > Vol. XXII, N° 1 (03/2007) . - p. 105-116 Titre : Correlation between injection molding parameters, morphology and mechanical properties of PPS/SEBS blend using artificial neural networks Type de document : texte imprimé Auteurs : C. Lotti, Auteur ; R. E. S. Bretas, Auteur Année de publication : 2007 Article en page(s) : p. 105-116 Note générale : Bibliogr. Langues : Anglais (eng) Index. décimale : 668.9 Polymères Résumé : 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. DOI : 10.3139/217.0991 En ligne : https://drive.google.com/file/d/115Z9gMooN7hT1N3if7T4SEz2XX7zHG4u/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=2774 [article]

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Correlations between injection molding parameters, morphology and mechanical properties of PPS using artificial neural networks / C. Lotti in INTERNATIONAL POLYMER PROCESSING, Vol. XXI, N° 2 (05/2006)  

[article]  inINTERNATIONAL POLYMER PROCESSING > Vol. XXI, N° 2 (05/2006) . - p. 104-115 Titre : Correlations between injection molding parameters, morphology and mechanical properties of PPS using artificial neural networks Type de document : texte imprimé Auteurs : C. Lotti, Auteur ; R. E. S. Bretas, Auteur Année de publication : 2006 Article en page(s) : p. 104-115 Note générale : Bibliogr. Langues : Anglais (eng) Index. décimale : 668.9 Polymères Résumé : The processing conditions of injection molding have a complex influence on the morphology and on the mechanical properties of a semicrystalline polymer. Therefore, to establish correlations between processing conditions, morphology and mechanical properties constitutes a difficult task; finding these correlations is one of the goals of a materials engineer. The purpose of this work was to study the influence of the injection molding conditions on the morphology and mechanical properties of poly(p-phenylene sulphide), PPS, using artificial neural networks, ANNs. First, a statistical analysis was done to find the more influential processing parameters that affect the morphology and mechanical properties of the PPS. Second, ANNs were applied to establish correlations between processing conditions, morphology and properties. It was found that the variables with the highest influence on the morphology and the mechanical properties were the injection and mold temperatures (Tinj and Tmold, respectively), as they showed a straight relationship with the crystallinity index of the injection molded part. Three different ANNs were built to predict the correlations. The ANN-1 predicted the crystallinity gradient along the thickness of the injection molded part from Tmold, Tinj, and flow rate, Q; the ANN-2 predicted the elastic and flexural modulus, E', and the yield stress from the crystallinity gradient, while the ANN-3 predicted the mechanical properties directly from the processing conditions. All ANNs were built with only fifteen experimental data and were trained with the group cross-validation method, GCV and with a training-test set method. Both methods showed similar and excellent performance. Thus, it can be concluded that ANNs can be used as a powerful tool in the learning of these complex correlations. DOI : 10.3139/217.0050 En ligne : https://drive.google.com/file/d/1gbrjdm-CrLMdAveK9Tl608ehtw47aARy/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=2903 [article]

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