Quality monitoring in a bottling line

This document describes the quality monitoring use case

2 minute read

Profile

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

Challenges

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

Solution

Integration

TODO: #67 Add integration for DCC quality check

Installed hardware

factorycube

A machine learning model runs on the factorycube, which evaluates and classifies the images. See also [factorycube].

Gateways

Gateways connect the sensors to the factorycube.

Models:

  • ifm AL1352

Light barriers

A light barrier identifies the bottle and sends a signal to the factorycube to trigger the camera.

Models:

  • ifm O5D100 (Optical distance sensor)

Camera

A camera takes a picture of the bottle and sends it to the factorycube.

Models:

  • Allied Vision (Mako G-223)

Detectable quality defects

Automated action

As soon as a quality defect is detected the defect bottle is automatically kicked out by the machine.