The Challenge.

  • Quality testing is an important part of a manufacturing production line, but this can present challenges. 
  • When quality control is done by humans (as it often is), it can bottleneck the production process in an impactful way.
  • Moreover, only a sample of the produced goods is inspected, and it is assumed that this is representative of the entire batch.

The Solution.

  • Designed and built a Machine Learning Computer Vision model to identify quality issues based on images of produced goods.
  • Integrated the solution directly into the production line to detect issues in real time.
  • Built a dashboard for real-time analysis of quality.

The Results.

  • Lower production costs by reducing time required for quality control.
  • Quality control in real-time enables production managers to react to issues more quickly.
  • An automated system enables the inspection of every part being produced instead of just a sample, improving the quality overall.  
style="vector-effect: non-scaling-stroke;">