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Open AccessArticle

Improving Composite Tensile Properties during Resin Infusion Based on a Computer Vision Flow-Control Approach

Departamento de Ingeniería Mecánica y Minera, Campus Las Lagunillas, Universidad de Jaén, 23071 Jaén, Spain
Author to whom correspondence should be addressed.
Materials 2018, 11(12), 2469;
Received: 29 October 2018 / Revised: 28 November 2018 / Accepted: 1 December 2018 / Published: 5 December 2018
(This article belongs to the Special Issue Advanced Materials for Transport Applications)
Liquid composite manufacturing techniques, mainly applied in the transport industry, have been studied and optimized for decades while defect analysis and its minimization have been a goal to increase reliability and mechanical performance. Researchers have found that many process parameters have a strong influence on the mechanical behavior of composite structures where the flow front velocity, closely related to voids, plays a considerable role. In this work, the optimal flow front velocity was evaluated and controlled using a computer vision system for different laminates improving the mechanical tensile properties and void content. Enhanced mechanical tensile properties were found using a feedback flow-controller vision system which was able to keep the optimal flow front velocity constant to reduce the air traps among tows and fibers. Tensile strength was enhanced up to 18% for fiber orientation at 0° and 3.3% at 90°, whereas tensile modulus was increased up to 18.4% for fibers at 0° and 8.7% at 90°. A novel methodology is presented through this work, aiming to improve the robustness of resin film infusion (RFI) processes while ensuring the quality of the composite material. View Full-Text
Keywords: composite; tensile; optimization; automotive; lightweight design composite; tensile; optimization; automotive; lightweight design
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Almazán-Lázaro, J.-A.; López-Alba, E.; Díaz-Garrido, F.-A. Improving Composite Tensile Properties during Resin Infusion Based on a Computer Vision Flow-Control Approach. Materials 2018, 11, 2469.

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