Real-time defect monitoring is crucial in manufacturing, as it allows companies to detect and address issues as they arise, rather than after the fact. Our AI models are now being employed to help with this task, using machine learning algorithms to analyze data and identify potential defects in real-time.

The architecture of these AI models typically involves a combination of sensors, cameras, and other monitoring equipment, which collect data on the manufacturing process. This data is then analyzed by machine learning algorithms, which are trained on historical data to identify patterns and anomalies in the production process that could indicate defects.

By identifying and addressing issues as they occur, companies can reduce waste, improve product quality, and increase efficiency. Additionally, the use of AI models allows for more consistent and accurate monitoring, reducing the risk of human error.