Accuracy Analysis and Error Source Identification for Optimization of Robot Based Machining Systems for Aerospace Production

Paper #:
  • 2016-01-2137

Published:
  • 2016-09-27
DOI:
  • 10.4271/2016-01-2137
Citation:
Kothe, S., Stürmer, S., Schmidt, H., Boehlmann, C. et al., "Accuracy Analysis and Error Source Identification for Optimization of Robot Based Machining Systems for Aerospace Production," SAE Technical Paper 2016-01-2137, 2016, doi:10.4271/2016-01-2137.
Pages:
7
Abstract:
Strong market growth, upcoming global competition and the impact of customer-requirements in aerospace industry demand for more productive, flexible and cost-effective machining systems. Industrial robots have already demonstrated their advantages in smart and efficient production in a wide field of applications and industries. However, their use for machining of structural aircraft components is still obstructed by the disadvantage of low absolute accuracy and adverse reaction to process loads. This publication demonstrates and investigates different methods for performance assessment and optimization of robot-based machining systems. For conventional Cartesian CNC machining systems several methods and guidelines for performance assessment and error identification are available. Due to the attributes of a common 6-axis-robot serial kinematics these methods of decoupled and separated analysis fail, especially concerning optimization of the system. One main focus of this paper lies on a new performance assessment strategy that in contrast to conventional methods neither needs a machining process nor an additional measurement system. Nevertheless it can be combined with these methods to provide even better results. By plotting the robots encoder during movement, calculating the actual tool-position/orientation and visualizing the hypothetic part manufactured a virtual machining process is elaborated. The effectiveness of this approach is demonstrated in combination with the robot optimization strategies “real-time guidance with LaserTracker” and “control parameter optimization”. The two optimization strategies themselves are the second main focus of the investigations presented.
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