Towards more Agility in Robot Painting through 3D Object Recognition

Pichler, Andreas; Bauer, Harald; Eberst, Christof; Heindl, Christoph; Minichberger, J.

doi:10.1016/b978-008045157-2/50106-1

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…Moreover, the automation of other processes involving dispensing of viscous liquids similar to the masking process has been previously investigated such as: robotic sealing of aerospace parts 5 , robotic coating for space solar modules 6 , robotic spray painting for automotive parts 7,8 , robotic workstations for small volume liquid dispensing and handling in laboratories 9 . In addition, automation of different processes in the aircraft manufacturing and assembly is being increasingly sought after 10,11 .…”

Section: Introductionmentioning

confidence: 99%

Towards an automated masking process: A model-based approach

Elgeneidy

Al-Yacoub

Usman

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part B:

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The masking of aircraft engine parts, such as turbine blades, is a major bottleneck for the aerospace industry. The process is often carried out manually in multiple stages of coating and curing, which requires extensive time and introduces variations in the masking quality. This paper investigates the automation of the masking process utilising the wellestablished time-pressure (T/P) dispensing process for controlled maskant dispensing, and a robotic manipulator for accurate part handling. A mathematical model for the T/P dispensing process was derived, extending previous models from the literature by incorporating the robot velocity for controlled masking line width. An experiment was designed, based on the theoretical analysis of the dispensing process, to derive an empirical model from the generated data that incorporates the losses that are otherwise difficult to model mathematically. The model was validated under new input conditions to demonstrate the feasibility of the proposed approach and the masking accuracy using the derived model.

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