Quality monitoring in a bottling line
This document describes the quality monitoring use case
2 minute read
A bottling line for filling water bottles was retrofitted with an artificial intelligence quality inspection system. With the help of a camera connected to an ia: factorycube, the bottles are checked for quality defects and sorted out by a pneumatic device in the event of a defect.
Photos of the machines
Manual visual inspection causes high costs
- Each individual bottle is checked for quality defects by an employee
- One employee is assigned to each shift exclusively for quality inspection
Customer complaints and claims due to undetected quality defects
- Various quality defects are difficult to detect with the naked eye and are occasionally overlooked
No data on quality defects that occur for product and process improvement
- Type and frequency of quality defects are not recorded and documented
- No data exists that can be analyzed to derive improvement measures for product and process optimization
TODO: #67 Add integration for DCC quality check
A machine learning model runs on the factorycube, which evaluates and classifies the images. See also [factorycube].
Gateways connect the sensors to the factorycube.
A light barrier identifies the bottle and sends a signal to the factorycube to trigger the camera.
- ifm O5D100 (Optical distance sensor)
A camera takes a picture of the bottle and sends it to the factorycube.
- Allied Vision (Mako G-223)
Detectable quality defects
As soon as a quality defect is detected the defect bottle is automatically kicked out by the machine.