Influência das condições de processamento na morfologia e das propriedades mecânicas de sistemas poliméricos moldados por injeção e sua predição através de redes neurais artificiais

• Ano de defesa: 2004
• Autor(a) principal: Lotti, Cybele
• Orientador(a): Bretas, Rosario Elida Suman
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência e Engenharia de Materiais - PPGCEM
• País: BR
• Palavras-chave em Português: Termoplásticos; Moldagem por injeção de plástico; Redes neurais (computação); Morfologia de blendas; Propriedades mecânicas
• Palavras-chave em Inglês: Morphology; Crystallinity; Blend; Mechanical properties; Injection molding; Experimental design; Artificial neural networks
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA DE MATERIAIS E METALURGICA
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/665

The influence of the injection molding parameters on the morphology and mechanical properties of poly(phenylene sulfide), PPS, and of a 80/20(%wt) blend of PPS/block copolymer styrene-ethylene-butylene-styrene ,SEBS, were evaluated. The injection molding conditions were defined through an experimental design. The lower and upper limits of each variable were set considering the material characteristics and the machine capacity. The software MOLDFLOW® was used to simulate the injection molding process, to define the cooling and holding times, to guarantee the part quality and to obtain the shear rate and bulk temperature profiles at the end of the filling step. For PPS, it was observed that the variables with highest influence on the gradient of crystallinity along the part thickness and on the mechanical properties were melt (Tinj) and mold (Tm) temperatures. For the PPS/SEBS blend, the flow rate (Q), mold temperature and holding pressure (Ph) were the variables with highest influence on the morphology. The aspect ratio of the SEBS particles, dispersed on the PPS matrix, was almost unaffected by the changes of the injection conditions; on the other hand, the mean particle size (caliper length along the major axis) and the value of the dispersion function represented qualitatively well the morphological variations observed for the blend. The artificial neural networks, built with experimental data and trained with the group cross validation method (GCV), predicted with good precision the morphology and the mechanical properties, starting from the injection molding processing conditions, as well as the mechanical properties starting from the morphological aspects.