Automotive Assembly Line Quality Inspection

Deployed AI-powered visual inspection system across 12 production lines, achieving 99.7% defect detection accuracy and reducing false positives by 85%.

The Challenge

A major automotive manufacturer needed to improve quality control on their engine assembly lines. Traditional vision inspection systems produced too many false positives (>30%), causing unnecessary line stoppages and wasting inspector time. The harsh factory environment (-10°C to 45°C, heavy vibration, dust) made commercial computing solutions unreliable. Existing systems lacked the computational power for deep learning-based inspection, limiting detection accuracy.

The Solution

We deployed 24 units of NAI-6000 edge AI platforms equipped with NVIDIA RTX 3060 GPUs across 12 assembly lines. Each system runs custom-trained YOLOv8 models for real-time defect detection on critical engine components. The fanless design eliminated reliability issues from dust and vibration. Local inference processing reduced network bandwidth requirements by 95% compared to cloud-based solutions. Integration with existing MES and SCADA systems enabled automatic line control and quality reporting.