Browse Publications Technical Papers 2019-01-1886
2019-09-16

Intelligent Real Time Inspection of Rivet Quality Supported by Human-Robot-Collaboration 2019-01-1886

Aircraft production is facing various technical challenges, such as large product dimensions, complex joining processes and the organization of assembly tasks. Meeting the requirements that come with large dimensions, low tolerances and small batch sizes, in combination with complex joining processes, automation and labor-intensive inspection task, is often difficult to achieve in an economically viable way.
ZeMA believes that a semi-automated approach is the most effective for optimizing aircraft section assembly. An effective optimization of aircraft production can be achieved with a semi-automated riveting process for solid rivets using Human-Robot-Collaboration in combination with an intuitive Human-Machine-Interaction operating concept.
While using dynamic task sharing between human and robot based on their skills, and considering ergonomics, the determined ideal solution involves placing a robot inside the section barrel. The robot’s workspace is expanded by mounting it on top of a lifting unit so that it can properly position the anvil. In the meantime, the human performs the more complex tasks of inserting the solid rivets and operating the riveting hammer from the outside of the section barrel.
By implementing a modular control system for configuration and operation of the assembly station with a variety of interaction possibilities, human and robot can perform the collaborative riveting process effectively. Additionally, due to high forces and vibrations, which are applied by the riveting hammer, a process specific tool has been developed to prevent damage to the robot system. By equipping the tool with sensors, such as a force torque and laser line sensor, it can not only monitor the riveting process in real time, but also perform inspection tasks using artificial intelligence algorithm. The sensor data will be passed through a trained machine learning model classifying whether the scanned part is of good or bad quality. With a limited amount of data, the “online” rivet classification using sensor data reaches a classification precision of 86%, whereas the rivet classification based on pictures reaches a precision of 97%. The result of the quality inspection will be sent to the employee using Mixed Reality glasses. This will allow the operator to react appropriately and carry out necessary maintenance.
The results are part of the project "Modularity, Safety, Usability, Efficiency by Human-Robot-Collaboration - FourByThree" (no 637095), funded by European Union's Horizon 2020 research and innovation program at ZeMA and will present semi-automation shown in the HRC riveting process. Furthermore the research is funded by the Interreg V A Großregion within Robotix-Academy project (no 002-4-09-001). The rivet quality inspection using artificial intelligence has been supported by PIKON Deutschland AG.

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