1. Improving robotic machining accuracy through experimental error investigation and modular compensation
- Author
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Ulrich Schneider, Francesco Leali, Alexander Verl, Christian Lehmann, Marcello Pellicciari, Manuel Drust, Matteo Ansaloni, Jan Willem Gunnink, and Publica
- Subjects
Robot dynamics ,0209 industrial biotechnology ,Engineering ,Typical set ,Industrieroboter ,02 engineering and technology ,Kinematics ,Robotic machining, Robot dynamics, Robot modelling, Error compensation, Optical tracking ,Industrial and Manufacturing Engineering ,Compensation (engineering) ,Computer Science::Robotics ,Robotic machining ,020901 industrial engineering & automation ,Machining ,Error compensation ,Fehlerkompensation ,Fräsen ,Simulation ,robotics ,Basis (linear algebra) ,business.industry ,Mechanical Engineering ,Control engineering ,Tracking system ,tracking ,Modular design ,021001 nanoscience & nanotechnology ,Computer Science Applications ,Robot modelling ,Fehleranalyse ,Control and Systems Engineering ,Robot ,Optical tracking ,0210 nano-technology ,business ,Software ,machining - Abstract
Machining using industrial robots is currently limited to applications with low geometrical accuracies and soft materials. This paper analyzes the sources of errors in robotic machining and characterizes them in amplitude and frequency. Experiments under different conditions represent a typical set of industrial applications and allow a qualified evaluation. Based on this analysis, a modular approach is proposed to overcome these obstacles, applied both during program generation (offline) and execution (online). Predictive offline compensation of machining errors is achieved by means of an innovative programming system, based on kinematic and dynamic robot models. Real-time adaptive machining error compensation is also provided by sensing the real robot positions with an innovative tracking system and corrective feedback to both the robot and an additional high-dynamic compensation mechanism on piezo-actuator basis.
- Published
- 2014