Overview

A major aerospace manufacturer experienced assembly delays due to the inefficient manual inspection of TC Clips, Angles, Fasteners, and fuselage holes. To resolve this, they implemented an AI-powered vision inspection system with a motorized tripod for automated image capture. The system improved part traceability, ensured correct orientation, and validated the dimensions and positions of holes. By maintaining a comprehensive digital log of inspections, non-conformities were detected earlier, reducing rework costs by 20% and enhancing lead times by 30%. This AI-driven solution significantly increased inspection accuracy and operational efficiency, meeting stringent aerospace quality standards.

Challenge

The main challenges centered around ensuring that hundreds of TC Clips, Angles, and Fasteners were accurately positioned and installed according to the engineering drawings. Misalignments during manual inspections caused delays in later assembly stages. Moreover, there was no system to verify hole conformity in terms of dimensions and positions, nor to ensure accountability for each installed component.

Solution

The aerospace manufacturer implemented an AI-powered vision inspection system developed by FoviaTech, which leveraged deep learning algorithms to improve image analysis for verifying part conformity. The system featured a motorized tripod mechanism, specially designed for inspecting the fuselage, allowing for automated image capture of all components, including those in difficult-to-reach areas. This ensured thorough visual inspection of TC Clips, Angles, Fasteners, and holes.

The AI-driven conformity check system delivered several key benefits: 

• Accelerated Inspections: Dramatically shortened lead times for verifying part conformity by automating the inspection of TC Clips, Angles, Fasteners, and holes.
• Increased Accuracy: Enhanced precision in part orientation and fastener placement, ensuring accurate hole dimensions and positions.
• Improved Traceability: Generated comprehensive inspection logs, including inspector ID, inspection dates, shipset numbers, and area coverage.
• Automated Non-conformity Detection: Facilitated early detection of non-conformities, reducing delays and minimizing rework.

Results

• The deployment of the AI-powered system achieved impressive outcomes.

   

• 30% reduction in lead time for inspecting TC Clips, Angles, and Fasteners.

   

• 95% improvement in part orientation accuracy, including precise placement of holes according to specifications.

   

• 25% decrease in delays during the assembly phase, due to the early detection of potential issues.

   

• 20% reduction in rework costs, thanks to early identification of non-conformities.