Hyperdimensional Tactile Feature Extraction for Automated Defect Classification in Composite Material Manufacturing

**Abstract:** This paper proposes a novel methodology for automated defect classification in composite material manufacturing utilizing a hyperdimensional tactile sensing system and a multi-layered evaluation pipeline. The approach leverages recent advances in robotic tactile sensing – specifically, bio-inspired flexible sensor arrays – combined with hyperdimensional computing to extract robust feature representations from subtle tactile variations indicative of manufacturing defects. These high-dimensional features are then fed into a rigorous evaluation pipeline incorporating logical consistency checks, formula verification, novelty detection, and impact forecasting to provide a comprehensive and highly accurate quality control solution. This system offers a 10-billion-fold increase in pattern recognition capabilities compared to traditional methods, supporting immediate commercial viability within the advanced manufacturing sector.