Next-generation perception system for automated defects detection in composite laminates via polarized computational imaging

TL;DR

This paper presents a polarized computational imaging system that effectively detects surface defects in composite laminates, enhancing quality control in manufacturing processes involving complex surfaces.

Contribution

The paper introduces a novel polarized imaging approach for defect detection in composite laminates, capable of identifying micro-geometry surface irregularities not visible in standard images.

Findings

Successfully detects surface defects like pinholes, voids, scratches, and resin flash.

Effective across different fiber types such as glass and carbon fibers.

Enhances surface defect detection beyond conventional imaging methods.

Abstract

Finishing operations on large-scale composite components like wind turbine blades, including trimming and sanding, often require multiple workers and part repositioning. In the composites manufacturing industry, automation of such processes is challenging, as manufactured part geometry may be inconsistent and task completion is based on human judgment and experience. Implementing a mobile, collaborative robotic system capable of performing finishing tasks in dynamic and uncertain environments would improve quality and lower manufacturing costs. To complete the given tasks, the collaborative robotic team must properly understand the environment and detect irregularities in the manufactured parts. In this paper, we describe the initial implementation and demonstration of a polarized computational imaging system to identify defects in composite laminates. As the polarimetric images are highly relevant to the surface micro-geometry, they can be used to detect surface defects that are not visible in conventional color images. The proposed vision system successfully identifies defect types and surface characteristics (e.g., pinholes, voids, scratches, resin flash) for different glass fiber and carbon fiber laminates.