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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.
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.
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.
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