Die kollaborative Teilebearbeitung mit Robotern und Werkzeugmaschinen wird in der Fertigung immer wichtiger. Durch die gleichzeitige Arbeit von Robotern und Werkzeugmaschinen kann eine noch effizientere Produktion erreicht werden. Mit der Entwicklung fortschrittlicher simulationsbasierter Lösungen können Hersteller jetzt die Zusammenarbeit von Robotern und Werkzeugmaschinen besser simulieren und überprüfen, um potenzielle Probleme zu erkennen und zu lösen, bevor sie in der realen Fertigung auftreten. Eine solche Lösung ist die NX-Offline-Simulation in Verbindung mit digitalen Zwillingen, mit der Hersteller ihre Teilebearbeitungsprogramme testen und optimieren können. Der Vortrag erläutert kurz das laufende Projekt 'RokoPro' und die Notwendigkeit des Einsatzes von Robotern in der Fertigung und der Simulation zur Überprüfung dieser Lösungen. / Collaborative part processing with robots and machine tools is becoming increasingly important in manufacturing. Even more efficient production can be achieved by having robots and machine tools work simultaneously. With the development of advanced Simulation-based solutions manufacturers can now better simulate and verify the collaboration of robots and machine tools to identify and solve potential problems before they occur in real manufacturing. One such solution is NX offline simulation along with digital twins, which allows manufacturers to test and optimize their part processing programs. The presentation briefly explains the ongoing project “RokoPro” and the need for use of robots in manufacturing and simulation to check those solutions.

Manufacturing process development is under constant pressure to achieve a good yield for stable processes. The development of new technologies, especially in the field of photomask and semiconductor development, is at its phys- ical limits. In this area, data, e.g. sensor data, has to be collected and analyzed for each process in order to ensure process quality. With increasing complexity of manufactur- ing processes, the volume of data that has to be evaluated rises accordingly. The complexity and data volume exceeds the possibility of a manual data analysis. At this point, data mining techniques become interesting. The application of current techniques is complex because most of the data is captured with sensor measurement tools. Therefore, every measured value contains a specific error. In this paper we propose an error-aware extension of the density-based al- gorithm DBSCAN. Furthermore, we present some quality measures which could be utilized for further interpretation of the determined clustering results. With this new cluster algorithm, we can ensure that masks are classified into the correct cluster with respect to the measurement errors, thus ensuring a more likely correlation between the masks.