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Aerospace , Automotive , and Robotics Aerospace , Automotive , and Robotics

In Process Alignment , In Process Verification , and Material Handling In Process Alignment , In Process Verification , Material Handling , Measurement (Non Contact) , Process Control , Simulation/3D Modeling , System Analysis & Verification , and Vision Guidance for Robotics

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Robot Guidance: Automated Wheel Installation

POSTED 02/19/2020  | By: Alexie, Product Manager

The Application

The manufacturer in the automotive sector was looking for a solution for the wheel installation in order to improve processes in its factory. This manufacturer was using a manual system that required four workers to pick-up and mount each tire on the rotor; two workers would work on each side of the wheel during the installation process.

Figure 1: acquisition of the rotor cloud points with the cirrus3D by VISIO NERF (structured light + stereovision)This application has been realized by Bluewrist.

The Challenge

There were a number of challenges to address when designing an application that would improve performance, process, and quality. These challenges relate to both the rotors and the processes involved in fitting the tires.

  •  Over 60 different rims are used with different types of surfaces (dark, matte, glossy), which makes 2D camera systems difficult to implement because of the effect of lighting solutions on the different types of surfaces.
  • The bolts are preinstalled on each router. Each bolt cap has a small surface area, which means a 3D, high-resolution vision system is essential to accurately locate the point cloud for each bolt cap. That is why our partner has chosen the cirrus3D by VISIO NERF to develop its application. 
  • During installation, the rotor has a random rotation, which means that the bolts are in a different position for each installation, requiring a solution that will for and identify the bolt location.
  • Because the rotor can rotate up to 15 degrees in both directions along the car, the solution also needs to be able to achieve 3D matching or a large point cloud.
  • In addition to these technical factors, the part is heavy, and the cycle time is limited, with only 3.5 seconds available for the full point cloud grab and process.

Figure 2: acquisition of the wheel cloud points with the cirrus3D by VISIO NERF (structured light + stereovision)The Solution

The solution implemented is done with a high-resolution blue structured light 3D camera, the cirrus3D by VISIO NERF. The 3D sensor is combined to the Bluewrist 3D Vision Robot Guidance solution software. And two Fanuc robots with a seven-axis linear slide are used for the robotic aspect. Using this system, the point clouds of the wheel and the rotor are matched to the corresponding CAD model with remarkable accuracy. The vision system can achieve +/? 0.3 mm accuracy in XYZ and +/? 0.3 degrees in WPR.






Figure 3: cloud of points acquired by the 3D sensor cirrus3DThe Results

The full solution is scalable from a few hundred to thousands of cars per day as required by a manufacturer’s production requirements. Our client’s current production run rate is 250 cars per day, and the wheel installation system consistently achieves 99.5% uptime. The application can pick up the wheel and install it onto the rotor with multiple features covering the functionality required to process different surfaces, fitting positions, and bolts.

The 3D vision system is able to capture a high quality of cloud of points with different types of material. The system is robust to the color and brightness of the part: dark matt surface for the tire, bright shiny surface for the rotor for example. Moreover, the sensor is designed to make possible the simultaneous acquisition in a same scene of parts made of multiple materials. With a vision processing cycle time of 3.5 seconds, efficiency is much improved, the initial problems have been resolved, and the process no longer requires any worker involvement.

Figure 4: Rotor Matching with Bluewrist’s Software