Machining

We offer a solution that can compensate for both positional inaccuracy and deflection forces, enabling standard robots to achieve results normally provided by more costly solutions.

Cognibotics has add-on solutions to greatly improve the accuracy of machining with industrial robots. Industrial robots are very attractive for machining. With their large working volume, 6 DOF positioning, fast repositioning, and very low cost when compared to CNC machining centers, their potential is very high.

The difficulty in using industrial robots for machining comes from the inaccuracies of the robot in positioning, and from the deflections of the robot itself because of the high forces created during the machining cutting process. These forces cause the robot to deflect from the programmed path and the result may vary in the workspace. Past solutions have concentrated on using very stiff robots with extensive controller adjustments and with offline programming solutions to help in workpiece positioning.

Using the original robot controller and standard industrial robot, Cognibotics’ solution is able to calibrate the robot both for positional errors and for compensation of the machining forces using the robot’s own internal sensors. The result is the ability to machine harder materials with a higher accuracy.

Accuracies better than 0.25mm in aluminum machining with a standard industrial controller have been demonstrated.

Project Arobmach: Millbot

Millbot is a complete packaged solution for machining currently supporting ABB robots. The goal of the project is not only to show the technical feasiblity of 0.2 mm of accuracy when machining over a large volume with re-orientations, but also to show the ability to package the solution as an add-on to existing industrial robot controllers. The fundamental concept is to use the deep knowledge of the robot’s gearbox and link flexibilities combined with accurate kinematic identification to produce unique accuracy during a machining process.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 826851.

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