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Enhanced Low-Velocity Impact Properties for Resin Film Infusion-Manufactured Composites by Flow-Control Approach.

Juan-Antonio Almazán-Lázaro¹, Elías López-Alba¹, Sebastian Schmeer²

¹Department of Mechanical and Mining Engineering, Campus Las Lagunillas, University of Jaen, 23071 Jaén, Spain.

|October 13, 2021

Summary

This study introduces a computer vision method to reduce defects like voids in resin film infusion (RFI) composites. Optimizing impregnation velocity significantly enhances mechanical and impact properties for aerospace applications.

Keywords:

composite impact manufacturing optimization polymer reinforced stitching

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Area of Science:

Materials Science
Aerospace Engineering
Manufacturing Processes

Background:

Optimizing composite mechanical properties is crucial for sustainable aeronautics, with voids being a key defect in resin film infusion (RFI) manufacturing.
Minimizing defects like voids directly improves mechanical properties and enables lightweight composite designs.
Enhanced impact properties are essential for safety and reliability in aerospace applications.

Purpose of the Study:

To develop and apply a novel computer vision methodology for controlling impregnation velocity in RFI processes.
To reduce void content and enhance the impact properties of composite materials.
To evaluate the effectiveness of the methodology on both conventional and stitching-reinforced composites.

Main Methods:

A computer vision-based approach was implemented to monitor and control the impregnation velocity during the RFI process.
Impregnation velocity was systematically analyzed and optimized to minimize void formation.
The methodology was applied to conventional and stitching-reinforced composite samples, followed by impact testing.

Main Results:

Optimized impregnation velocity led to a significant reduction in void content within the composites.
Enhanced drop-impact properties were observed following the optimization of impregnation velocity.
Stitching-reinforced composites showed an improved damage threshold load, peak force, and reduced damaged area, despite potential increases in voids.

Conclusions:

The novel computer vision methodology effectively controls RFI processes, reduces defects, and enhances mechanical and impact properties.
The approach offers a pathway to improved automation and control in composite manufacturing, increasing industrial interest.
The study demonstrates a positive balance in mechanical properties for stitching-reinforced composites, highlighting the benefits of defect reduction.