Analysis and Reduction of Errors in an Automated Robot-Based Repair Process Chain

Abstract:

Even though modern industrial robots have good repeatabilities, their positioning accuracies are still relatively poor. Moreover, in a complex process chain, involving several handling systems and diverse interdependent tasks, error propagation can make matters worse. In order to achieve the overall desired quality level, intelligent and highly adaptive methods are required to reduce individual errors and remove accuracy couplings as much as possible. This is especially true in high-risk applications, as found in the aviation MRO industry. Because of the difficulty to replicate existing manual MRO accuracy levels, automation in this area is still relatively scarce. For instance the inspection and repair of airplane combustion chamber liners are as yet performed fully manually. In this paper an automated version of the entire liner repair chain is introduced: from robot-guided white light interferometer inspection in a first cell, to part and data transfer to a second robot cell through to the automated repair steps. Particular consideration is given to individual error sources, such as robot and sensor inaccuracies, calibration deviations and the transfer of data between robot cells, as well as error propagation and prevention.

• Periodical: Applied Mechanics and Materials (Volume 840)
• Pages: 16-23
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.840.16
• Online since: June 2016
• Authors: Christoph Schwienbacher*, Tomas Domaschke, Marc Andre Otto, Tobias Kötter, Thorsten Schüppstuhl
• Keywords: Accuracy Improvement, Aircraft MRO, Error Minimization, Industrial Robots, White Light Interferometry

References

[1] C. Schwienbacher, T. Kötter and T. Schüppstuhl, Repair of Aircraft Combustors with Industrial Robots, Proc. of ISR/Robotik, München, (2014).

[2] T. Domaschke, T. Schüppstuhl and M. -A. Otto, Robot Guided White Light Interferometry for Crack Inspection on Airplane Engine Components, Proc. of ISR/Robotik, München, (2014).

[3] Leibniz Universität Hannover, Collaborative Research Centre 871: Regeneration of Complex Capital Goods. Information on www. sfb871. de [04. 01. 2016].

[4] Y. Li, M. Kästner and E. Reithmeier, Development of a compact low coherence interferometer based on GPGPU for fast microscopic surface measurement on turbine blades, SPIE Optical Metrology: SPIE. Munich, Germany, (2015).

DOI: 10.1117/12.2184749

[5] J. Beyerer, F. Puente León and C. Frese, Automatische Sichtprüfung, Berlin, Heidelberg, Springer, (2012).

DOI: 10.1007/978-3-662-47786-1

[6] Z. Gan and Q. Tang, Visual sensing and its applications, Hangzhou, Zhejiang University Press, (2011).

[7] R. Schwarte, G. Häusler and R. W. Malz, Three-Dimensional Imaging Techniques, in Computer vision and applications: A guide for students and practitioners, San Diego: Academic Press, 2000, p.178–208.

DOI: 10.1016/b978-012379777-3/50008-x

[8] T. Kötter, T. Domaschke, C. Schwienbacher and T. Schüppstuhl, Workcell calibration for robot based maintenance of airplane engine components, Proceedings of the 5th International Workshop on Aircraft System Technologies, Aachen: Shaker. Hamburg, (2015).

[9] M. -A. Otto, T. Domaschke and M. Hermann, Hochgenaue 3D-Schadensdetektion an rotationssymmetrischen Maschinenteilen mit robotergeführtem Weißlichtinterferometer, Automation 2013: Die Chancen der Cloud, Baden Baden, (2013).

[10] P. J. Besl and N. D. McKay, Method for registration of 3-D shapes, Robotics - DL tentative: SPIE. Boston, MA, 1992, p.586–606.

[11] M. Otto, T. Domaschke, S. Bahr and M. Schnell, Präzise Rissdetektion und -bewertung an Flugzeugturbinenlinern mittels Weißlichtinterferometrie, Automation 2014: Smart X – Powered by Automation, Düsseldorf: VDI-Verl. Baden-Baden, (2014).

[12] J. Canny, A Computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, no. 6, p.679–698, (1986).

DOI: 10.1109/tpami.1986.4767851

[13] Z. Guo and R. W. Hall, Parallel thinning with two-subiteration algorithms, Commun. ACM, vol. 32, no. 3, p.359–373, (1989).

DOI: 10.1145/62065.62074

[14] H. A. Richard and M. Sander, Ermüdungsrisse: Erkennen, sicher beurteilen, vermeiden, 1st ed, Wiesbaden, Vieweg + Teubner, (2009).

DOI: 10.1007/978-3-8348-9232-4

[15] E. Roos, Anwendungsorientierte Meß- und Berechnungsverfahren zur Kalibrierung off-line programmierter Roboterapplikationen, Düsseldorf, VDI, (1998).

[16] C. Schwienbacher, F. Meiners, T. Kötter and T. Schüppstuhl, Improvement of Multi-directional Positioning Path Accuracy of Industrial Robots with Rotary Tables, Proceedings of the International Conference on Mechanical Engineering and Automation Science, Hong Kong, (2015).

DOI: 10.4028/www.scientific.net/amm.826.134