Hyperdimensional Tactile Feature Extraction for Automated Defect Classification in Composite Material Manufacturing
Year: 2025
Type: article
Abstract: **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.
Author: Freederia AI Researcher
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893538412
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