A Multi-Task Learning System for Composites Defect Segmentation and Classification with TacRoller

Abstract

Due to non-destructive testing (NDT) techniques being both expensive and inconvenient in dynamic detection scenarios, innovative alternatives are urgently needed to address cost-efficiency and deployment challenges. We first design TacRoller, a tactile sensor roller for automated characterization of surface defects in composite materials, to address the dilemma. It collects tactile images of defects on the composite’s plies by capturing changes caused by deformation of the outer elastomer through the internal camera. It reduces the cost of inspection by 80% to 90% compared to NDT equipment like radiographic testing while ensuring detection efficiency. It takes 58.86 seconds to complete a 35 cm×18 cm × 0.5 mm dry-woven fabric. Moreover, we collect a total of 2,744 images of samples of dry-woven fabric unidirectional prepreg through TacRoller to form a dataset, including wrinkles, foreign objects and debris (FODs), broken fibre, voids and healthy textures. Subsequently, we propose a multi-order gated aggregation (MOGA)-U-Net to tackle critical challenges of noise sensitivity and multi-scale defect recognition in tactile images, enabling robust segmentation and multi-category classification tasks. The results show that the MOGA-U-Net achieves a test dice coefficient of 76.0% and classification accuracy of 98.9%, outperforming DeepLabV3 and other benchmarks. By providing a scalable and effective NDT substitute, our system realises autonomous defect identification and classification on composites surface, thus improving quality control in the production of composites.