Success Story

reading time: 4 min

Manufacturing

Energy & Utilities

Predictive Quality

Visual Inspection

Anomaly Detection

Context

In solar panel manufacturing quality control decides whether each module performs well and lasts. Even small defects such as misaligned cells, cracks, soldering issues or wiring errors can sharply reduce a module's energy output and service life. Manual inspection is often slow and error-prone. Defective panels can reach customers and damage the company's reputation.

Challenge

Our client, a leading solar panel manufacturer, faced challenges in detecting micro-defects such as slight misalignments or micro-cracks that were not visible during manual inspection. These defects could reduce the performance and longevity of the panels, leading to customer dissatisfaction and higher warranty costs. The client needed a more accurate, faster, and scalable solution to improve the assembly verification process.

Assignment

We proposed the integration of navio VISION, an AI-powered visual anomaly detection system, to be deployed on the production lines. The system uses high-resolution cameras to capture images of solar panels at various assembly stages, automatically identifying defects and ensuring high-quality output.

Solution

navio VISION is deployed in the production line. High-resolution cameras capture detailed top-down images of the solar panels at different stages of assembly. Machine learning models analyze these images and identify misaligned cells, cracks, soldering defects and wiring issues. The system flags defective modules for rework before they go to final assembly. Only panels that meet the quality standards are shipped to customers.

With navio VISION in the assembly verification process, quality control became more precise and efficient. Because micro-defects are detected early, the client cut inspection time by 40% and reduced scrap and rework by 30%. Costs went down, and 99% of panels now meet the required quality standards.

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