Challenge

Our client, an expert in the manufacture and sale of smart home appliances, sought to improve the quality of one of its production lines while reducing waste. A key focus was detecting microcracks on gas mixer valves, which, if left unnoticed, could lead to leakages or breakages during final assembly. Detecting these defects manually was too time-consuming, making automation essential for improving production efficiency, reducing rework costs, and enhancing product quality.

Nature of collaboration

To address this challenge, we worked with our client to implement an AI-driven visual inspection system. Through collaboration, we deployed a defect detection model trained to identify microcracks efficiently, ensuring a faster and more reliable inspection process.

Solution

The visual inspection system is operational in one of our client’s factories, integrated into a vision station with robotic arms. The system:

• Uses robotic arms to pick up and present valves to a precision camera.
• Captures detailed images analyzed by an AI algorithm to detect microcracks.
• Automatically sorts valves based on detection results, directing them either for recycling or production.

To develop the AI model, we first deployed a data annotation system, enabling our client’s team to build a labeled dataset. This dataset was used iteratively to train and refine the defect detection model, which now enables real-time inspection with a cycle time of under three seconds.

Schematic view of the Sagacify’s vision system deployed on a rotary
table with robotic arms manipulating parts to take pictures from all angels.

Impact