Composites may be the material of the future, lightweight, strong and capable of performance beyond metals, but inspecting them has long been stuck in the past. In many production lines, skilled workers still rely on eyesight and touch to identify defects, a process that is precise yet slow, and increasingly a bottleneck for manufacturers.
The challenge lies in the material itself. Defects in composites often hide within fibre weaves, subtle wrinkles, or beneath curved, layered surfaces that defy standardised inspection methods. Traditional automated vision systems have struggled here, frequently mistaking natural variations for flaws or missing genuine defects due to texture, glare, and complex geometries.
AI That Understands, Not Just Detects
A breakthrough is emerging in the form of Zetamotion's Spectron, an AI-powered inspection platform designed specifically for composites. Unlike conventional systems that depend on pre-defined rules or vast defect libraries, Spectron uses semantic learning to understand the principles behind a part's design and defect patterns.
The system generates curated synthetic training data from a single scan, eliminating the need for extensive manual data labelling. This allows Spectron to adapt to each unique part, distinguishing between acceptable variation and true defects, even when surfaces are non-uniform or inherently imperfect.
Source: zetamotion.com
