A robot inspector is looking over the shoulders of robot welders.

What’s new: Farm equipment maker John Deere described a computer vision system that spots defective joints, helping to ensure that its heavy machinery leaves the production line ready to roll.

How it works: Like other manufacturers, John Deere uses robotic welders to assemble metal parts on its machines for farming, forestry, and construction. But industrial-strength welding has a longstanding problem: Bubbles of gas can form inside a joint as it cools, weakening it. An action recognition model developed by Intel spots such defects in real time.

  • The model was trained on videos of good and bad welds. The clips were lit only by welding sparks, so that lighting conditions wouldn’t affect the model’s performance.
  • The model is deployed on a ruggedized camera perched on the welding gun 12 to 14 inches away from the molten metal.
  • When it detects a bad weld, it stops the robot and alerts human workers.

Behind the news: AI-powered quality assurance is gaining ground. Systems from Landing AI (a sister company to DeepLearning.AI) and others recognize defects in a growing number of manufacturing processes.

Why it matters: Skilled human inspectors are in short supply, expensive to hire, and not always able to inspect every joint in a factory full of robotic welders, so defects may go unnoticed until after a subpar part has become part of a larger assembly. A single welded part can cost up to $10,000. By spotting errors as they occur, computer vision can save manufacturers time and money.

We’re thinking: Good to see AI making sure the job is weld done

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