Template matching method for void identification and the correlation of the identified voids to the mechanical strength of resin covered fiber glass composite surface

• Main Authors: Rachmawati, Lulu Millatina, Bethaningtyas, Hertiana, Handayani, Ismudiati Puri, Jatmiko, Agus
• Format: Conference Proceeding
• Language: English
• Subjects: Composite materials; Cross correlation; Digital imaging; Fiberglass; Glass fiber reinforced plastics; Glass fibers; Image contrast; Image processing; Mechanical properties; Resins; Template matching; Tensile strength; Voids
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0122348

Void is one type of defect emerging in composite material due to the missing of some elements during the fabrication process. The presence of voids can affect the mechanical properties and increase the potential damage of the composite. Voids possess micrometer size and various shapes, which requires a micrometer resolution technique for identification. This study aims to identify, classify, and count the number of voids in resin-covered fiberglass composite digital images, as well as to correlate the voids with the composite tensile strength. For that purpose, we applied the template matching method in digital image processing combining with the normalized cross-correlation (NCC) method to analyze the digital images of the composites prepared by dry-, wet-, and rolled-lay-up methods. We observed various shapes and sizes of voids present in the composite surface and correlated them with the composite tensile strength. Even t hough the samples are prepared with the same fabrication processes, the number of voids on each sample are varied. However, only voids with a diameter size larger than 70µm were found to influence the tensile strength strongly. The composite tensile strength tends to decrease with the increase of large voids numbers, whereas no consistent relation is observed between the number of small voids and tensile strength. We also found that RGB image templates are more accurate for localized void identification with a strong contrast with its surrounding. This study reveals the potential of digital image processing methods for identifying and characterizing various void geometries in composites.