Optimization of injection molding process for SGF and PTFE reinforced PC composites using response surface methodology and simulated annealing approach / T. Yang in INTERNATIONAL POLYMER PROCESSING, Vol. XXVI, N° 5 (11/2011)  

[article]  inINTERNATIONAL POLYMER PROCESSING > Vol. XXVI, N° 5 (11/2011) . - p. 525-539 Titre : Optimization of injection molding process for SGF and PTFE reinforced PC composites using response surface methodology and simulated annealing approach Type de document : texte imprimé Auteurs : T. Yang, Auteur ; Y.-K. Yang, Auteur ; C.-Y. Chen, Auteur Année de publication : 2011 Article en page(s) : p. 525-539 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Composites à fibres de verre -- Moulage par injection Matières plastiques -- Moulage par injection Polycarbonates Polytétrafluoréthylène Tags : Polytétrafluoroéthylène  (PTFE)  Polycarbonage  (PC)  'Composite  fibres  verre  courtes'  'Moulage  injection'  'Méthodologie  surfaces  réponse'  'Recuit  simulé'  'Analyse  variance' Index. décimale : 668.4 Plastiques, vinyles Résumé : This study is analyzed variations of ultimate strength, friction coefficient and wear mass loss that depend on the injection molding techniques during the blending of short glass fiber (SGF) and polytetrafluoroethylene (PTFE) reinforced polycarbonate (PC) composites. A hybrid method including response surface methodology (RSM) and back-propagation neural network (BPNN) integrating simulated annealing algorithm (SAA) are proposed to determine an optimal parameter setting of the injection molding process. The specimens are prepared under different injection molding processing conditions based on a Taguchi orthogonal array table. The results of eighteen experimental runs were utilized to train the BPNN predicting ultimate strength, friction coefficient and wear mass loss. Simultaneously, the RSM and SAA approaches were individually applied to search for an optimal setting. In addition, the analysis of variance (ANOVA) was implemented to identify significant factors for the injection molding process parameters and the result of BPNN integrating SAA was also compared with RSM approach. The results of optimal parameters of injection molding process for the ultimate strength of x-direction and ydirection based on BPNN/SAA approach were increased 3.12%, and 6.18%, respectively. DOI : 10.3139/217.2488 En ligne : https://drive.google.com/file/d/11Y49qVP8C62-32DeiRZv1tuh9UL8Vh9A/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=12469 [article]

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Simulation study on optimization of injection molding process for thin-shell plastic parts via the Taguchi method and grey relational analysis / M.-T. Chuang in INTERNATIONAL POLYMER PROCESSING, Vol. XXIV, N° 1 (03/2009)  

[article]  inINTERNATIONAL POLYMER PROCESSING > Vol. XXIV, N° 1 (03/2009) . - p. 51-58 Titre : Simulation study on optimization of injection molding process for thin-shell plastic parts via the Taguchi method and grey relational analysis Type de document : texte imprimé Auteurs : M.-T. Chuang, Auteur ; Y.-K. Yang, Auteur Année de publication : 2009 Article en page(s) : p. 51-58 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Matières plastiques -- Moulage par injection Taguchi, Méthodes de (Contrôle de qualité) Index. décimale : 668.9 Polymères Résumé : This paper deals with the application computer-aided engineering integrated with statistical techniques to reduce warpage variation due to injection molding process parameters during the production of thin-shell plastic components. For this purpose, Moldflow simulation runs are carried out by utilizing the combination of process parameters based on a three-level L18 orthogonal array table. An optimal parameter combination of the injection molding process is obtained via grey relational analysis (GRA). By analyzing the grey relational grade matrix, the degree of influence for each controllable process factor onto warpage can be found. Additionally, the analysis of variance (ANOVA) is also applied to identify the most significant factor; the melt temperature and the packing pressure are found to be the most significant factors in the simulation process for an injection molding process of thin-shell plastic parts. DOI : 10.3139/217.2187 En ligne : https://drive.google.com/file/d/1LmQjnX9YVxJEoWxXIeBM8GabZYanIuJ_/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=4207 [article]

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