The influence of fabric architecture on impregnation behavior and void formation: Artificial neural network and statistical‐based analysis
Source abstract
Abstract
Abstract This work proposes an approach combining artificial neural networks (ANN) with statistical models to predict injection processing conditions for four reinforcement architectures: plain weave, bidirectional noncrimp fabrics, unidirectional fabrics (Uni) and random fiber mats (Random). Key results allow evaluating the velocity of the flow front by combining processing parameters and creating a three‐dimensional response surface based on a properly trained ANN. This investigation is based on a large number of experimental results. The key role played by some physical parameters was associated with predicting the impregnation behavior (velocity of the flow front) during resin injection. The main outcome aims to provide a better control of void content in terms of size and position to the four fibrous reinforcements considered.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Epoxy Resin Curing Processes · Mechanical Behavior of Composites · Composite Material Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Materials and Technology São Paulo State University Guaratinguetá São paulo Brazil
Department of Mechanical Engineering Aalto University Espoo Finland
Advanced Composites Research Group, School of Mechanical and Aerospace Engineering Queen's University Belfast Belfast UK
PPGE3M Federal University of Rio Grande do Sul Porto Alegre Brazil
Department of Material Engineering Federal University for Latin American Integration (UNILA) Foz do Iguaçu Paraná Brazil
Department of Mechanical Engineering, Research Center for High Performance Polymer and Composite Systems Polytechnique Montréal Montreal Québec Canada
Funding
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior · Fundação de Amparo à Pesquisa do Estado de São Paulo
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