VISIO NERF Case Studies Robotic Guidance: Automatic Wheel Mounting
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Robotic Guidance: Automatic Wheel Mounting

Robotic Guidance: Automatic Wheel Mounting - VISIO NERF Industrial IoT Case Study
Analytics & Modeling - Computer Vision Software
Discrete Manufacturing
Computer Vision
System Integration

There are many challenges to address when designing applications that can improve performance, flow, and quality. These challenges involve rotors and the process of installing tires:

  • Over 60 different edges are used for different types of surfaces (dark, matte, glossy), which makes 2D camera systems difficult to capture due to the influence of lighting solutions on different types of surfaces.
  • The bolts are pre-installed on every router. Each bolt cap has a small surface area, which means a 3D, high-resolution vision system is necessary to accurately locate the point cloud for each bolt cap. 
  • During installation, the rotor has random rotation, which means that the bolts are in different positions for each installation, and a solution is needed that will identify the bolt positions.
  • The solution also needed to be capable of 3D matching or large point clouds, as the rotor could rotate 15 degrees in both directions along with the vehicle.
  • In addition to these technical factors, the part is heavy and has a limited cycle time of only 3.5 seconds for full point cloud grabbing and processing.
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The solution implemented by Visio NERF uses a high-resolution blue light 3D camera. 3D sensors combined into a blue 3D Vision Robot to guide the solution software. Two FANUC robots with seven-axis linear slides are used in robotics. Using this system, point clouds of wheels and rotors are matched to the corresponding CAD models with remarkable accuracy.

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[Efficiency Improvement - Productivity]

3D vision systems are capable of capturing high-quality point clouds of different types of materials. The system is robust to colour and brightness parts: tires with a dull surface, rotors with a bright glossy surface. In addition, the design of the sensor makes it possible to simultaneously acquire parts made of multiple materials in the same scene. The visual processing cycle is 3.5 seconds, the efficiency is greatly improved, the initial problem has been solved, and the whole process no longer requires any worker participation.

Depending on the manufacturer's production needs, the complete solution can scale from a few hundred to thousands of vehicles per day. The customer's current production is 250 vehicles per day and the uptime of the wheel mounting system is consistently 99.5%. The program picks up the wheel and mounts it to the rotor, with multiple functions, including those needed to handle different surfaces, mounting locations, and bolts.

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