During assembly, the worker simulates a faulty process by skipping the assembly of one of the tree screws.
After the worker put the screwdriver back to the position indicated by the guidance system, the system takes a picture using the camera attached to the robot and analyzes the picture.
In the background, the image taken by the robot is analyzed using a pre-trained machine learning model capable of detecting missing screws or other defects. Since the worker made a mistake, the system detects the missing screw during the visual inspection operation using the integrated AI-powered video inspection system.
The following business benefits are shown in this process step:
Enhanced Quality Control: The use of an AI-powered video inspection system ensures early detection of assembly defects such as missing screws, improving product quality.
Efficiency in Error Detection: Automating the inspection process reduces the time needed to identify errors, enabling quicker corrective actions and maintaining production pace.
Reduced Human Error Impact: By electronically confirming assembly completion and triggering AI inspection, the reliance on manual checks is minimized, reducing the risk of human error.
Consistent Inspection Standards: The pre-trained machine learning model applies uniform quality standards across all units, ensuring consistent inspection criteria are met.
Cost Savings: Early detection of assembly errors can prevent defective products from proceeding down the line, reducing waste and the cost associated with rework or recalls.
Data Utilization for Continuous Improvement: The system's ability to capture and analyze data supports ongoing evaluation and refinement of assembly processes.