Robot integration

Daniel Schilling,

Process optimization during robot operation

To optimize robot applications, teach points are readjusted manually. Robot operation must be interrupted for this. ArtiMinds now offers a solution for fully automatic optimization.

ArtiMinds LAR can be used to determine the optimum joining point for different workpiece carriers with different tolerances, thereby optimizing the cycle time © ArtiMinds Robotics

With the Learning & Analytics for Robots (LAR) software tool, the provider has developed a solution to specifically optimize relevant aspects of a robot program in terms of runtime and robustness and to continuously monitor processes.

Data analysis of the process data generated during operation of the robot is used for this purpose. This data can be automatically collected and annotated using the ArtiMinds RPS programming software without any additional programming effort. This means, for example, that the optimum starting point for a sensor-based search for a joining process can be determined very efficiently and transparently in LAR. The results can then be transferred offline to the RPS program to be optimized.

Fully automatic optimization

ArtiMinds now offers the option of using a PLC to enable fully automatic optimization without manual user input during ongoing robot operation, i.e. without stopping production. This acts as a gatekeeper between the LAR database backend and the robot controller, as the two have different requirements in terms of their communication protocols.

Furthermore, the PLC assumes the role of an additional safety instance that ensures that automatic optimizations can only take place to a safe extent and that guarantees uninterrupted robot operation even if the database is not available. Last but not least, an HMI on the PLC allows manual intervention in the optimization parameters or (de)activation of the optimization after successful user authentication.

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Using the described approach of robot, PLC and data-driven optimization, not only individual, general optimizations can be carried out. The system is able to learn optimizations for each specific component or workpiece carrier and use them at runtime to suit the current situation. A reliable, self-optimizing system consisting of established industrial components can therefore be implemented from a single source.

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