Abstract

This work presents the development of an image analysis software based on artificial intelligence for the detection and quantification of pore and void defects in carbon fiber reinforced polymer (CFRP) laminates. The tool aims to improve the inspection process of these materials, which is traditionally carried out through optical microscopy and manual analysis, methods that require significant time and effort from technical personnel. Machine learning and computer vision techniques, specifically the YOLOv8 instance segmentation model, were employed to automate the analysis of micrographs of fiber-reinforced composites (FRPs), including carbon and glass fibers. The results show a significant reduction in analysis time – from 30 minutes to just a few seconds – although the model’s accuracy is not yet sufficient for full implementation in a laboratory setting. While the results are acceptable in some cases, validation by qualified technicians remains necessary. This work is part of the ASSISTER project, funded by the Secretariat of State for Digitalization and Artificial Intelligence, under the Ministry of Economic Affairs and Digital Transformation, through the C005/21-ED call by red.es, and by the Andalusian Technological Corporation (CTA).