The influence of fabric architecture on impregnation behavior and void formation: Artificial neural network and statistical‐based analysis-Reference-Cited by-同舟云学术

The influence of fabric architecture on impregnation behavior and void formation: Artificial neural network and statistical‐based analysis

Published:2022-03-05 Issue:5 Volume:43 Page:2812-2823
ISSN:0272-8397
Container-title:Polymer Composites
language:en
Short-container-title:Polymer Composites

Author:

Monticeli Francisco M.¹^ORCID,Almeida José Humberto S.²³^ORCID,Neves Roberta M.⁴^ORCID,Ornaghi Heitor L.⁵^ORCID,Trochu François⁶^ORCID

Affiliation:

1. Department of Materials and Technology São Paulo State University Guaratinguetá São paulo Brazil

2. Department of Mechanical Engineering Aalto University Espoo Finland

3. Advanced Composites Research Group, School of Mechanical and Aerospace Engineering Queen's University Belfast Belfast UK

4. PPGE3M Federal University of Rio Grande do Sul Porto Alegre Brazil

5. Department of Material Engineering Federal University for Latin American Integration (UNILA) Foz do Iguaçu Paraná Brazil

6. Department of Mechanical Engineering, Research Center for High Performance Polymer and Composite Systems Polytechnique Montréal Montreal Québec Canada

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Fundação de Amparo à Pesquisa do Estado de São Paulo

Publisher

Wiley

Subject

Materials Chemistry,Polymers and Plastics,General Chemistry,Ceramics and Composites

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/pc.26578

Reference62 articles.

1. Evaluation of through-thickness permeability and the capillary effect in vacuum assisted liquid molding process

2. Multi-criteria thermal optimization in liquid composite molding to reduce processing stresses and cycle time

3. Modeling the impact of capillary pressure and air entrapment on fiber tow saturation during resin infusion in LCM

4. Infusion design methodology for thick-section, low-permeability preforms using inter-laminar flow media

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