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Leak TestComponent assemblyControl of surface defectsBin PickingServicesOther



The control of surface defects has always been a very complex type of application. On many occasions there has been no other solution than to assign this task to one person, in the absence of a better technical solution.

Of course this is not ideal given the cost, the risk of human error and fatigue. Luckily, the state of the art changes at a dizzying rate:

  •  Powerful algorithms such as photometric stereo allow us to better highlight surface defects.
  • The development of Deep Learning has enabled us to not be dependent on working out complex algorithms that cannot cover all cases, but instead let the machine itself learn, from images of good and bad parts, or whatever criteria is used to separate them.
Control de defectos superficiales

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