Automated Vision-Based Inspection of Drilled CFRP Composites Using Multi-Light Imaging

Deep learning for detecting defects in aerospace composite structures

Stage
Research Prototype
Published
Who made it?
Ali Maghami
Tags
Computer VisionDeep LearningInspectionAerospaceResearch
Share

Publication

Authors: Maghami, A., Salehi, M., and Khoshdarregi, M.

Journal: CIRP Journal of Manufacturing Science and Technology, 2021

Overview

This work explores using computer vision and deep learning to automatically detect defects in drilled composite structures, making quality control faster and more consistent.

Problem

Detecting microscopic defects around drilled holes in aerospace composites is time-consuming and inconsistent when done manually. We investigated whether automated vision could improve inspection processes.

Approach

We developed a system using multiple light angles to reveal different types of defects, then trained deep learning models to recognize damage patterns and anomalies automatically.

Results

The system achieved high accuracy for defect detection and significantly reduced inspection time compared to manual methods, with validation on real manufacturing samples.

Application

This work demonstrates the potential for automating quality control in precision composite manufacturing, where visual inspection is critical but challenging to automate effectively.