At Hammer-IMS, we make inline quality control smart, adaptive, and reliable. While simple mathematical rules are great for strict limits, our AI-powered software (connectivity 3.0) excels at recognizing complex, irregular material defects that standard rules might miss.
But how does the system learn to spot and sort these flaws perfectly for your specific factory line?
It follows a clear, 6-step journey. We start by capturing your standard material, involve your operators to share their experience, and use that knowledge to train a smart system.
Below is the step-by-step process of how we take your production line from raw camera images to a fully automated, 24/7 quality inspector.
Cameras capture high-resolution images during the early part of the project to build a baseline of your standard material.
One or multiple initial AI models are trained using the baseline images, allowing the software to successfully highlight anomalies.
During the commissioning phase, plant operators manually classify the found defects with letter codes or delete occasional false detections.
The operator's feedback is used to retrain the AI system, teaching the software to sort and recognize specific defect types completely on its own.
The system is deployed live in production. Operators can use specific recipes to fine-tune camera settings for different product groups and verify real-time performance.
The AI runs fully automated 24/7. It archives defect positions, sizes, and images directly to your servers, while triggering smart stack light alarms if defect limits are exceeded.
What you will learn in this video:
What you’ll learn in this video:
