About: Abstract Impact hammer experiments are typically used for identifying the Frequency Response Function (FRF) of six-degree-of-freedom (6-dof) industrial robots for machining applications. However, the modal properties of 6-dof industrial robots change as a function of robot arm configuration. Hence, describing the robot’s modal parameters within its workspace requires off-line impact hammer experiments performed at discrete robot end effector positions, which are costly and time consuming. Instead, it is more efficient to calculate the robot FRF using Operational Modal Analysis (OMA), a method that utilizes data acquired during the actual machining process. This paper presents an OMA approach to identify the robot FRF from measured milling forces and robot tool tip vibrations. Analysis of the milling process data reveal that periodic forces produced in the milling process are accompanied by background white noise that induce broadband excitation across the robot structure’s frequency spectrum. Hence, the tool tip vibration signal contains the signature of the structure’s free response that enables the use of OMA to estimate the robot’s FRF. The FRF calculated using OMA is shown to be in good agreement with results obtained from impact hammer experiments.   Goto Sponge  NotDistinct  Permalink

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  • Abstract Impact hammer experiments are typically used for identifying the Frequency Response Function (FRF) of six-degree-of-freedom (6-dof) industrial robots for machining applications. However, the modal properties of 6-dof industrial robots change as a function of robot arm configuration. Hence, describing the robot’s modal parameters within its workspace requires off-line impact hammer experiments performed at discrete robot end effector positions, which are costly and time consuming. Instead, it is more efficient to calculate the robot FRF using Operational Modal Analysis (OMA), a method that utilizes data acquired during the actual machining process. This paper presents an OMA approach to identify the robot FRF from measured milling forces and robot tool tip vibrations. Analysis of the milling process data reveal that periodic forces produced in the milling process are accompanied by background white noise that induce broadband excitation across the robot structure’s frequency spectrum. Hence, the tool tip vibration signal contains the signature of the structure’s free response that enables the use of OMA to estimate the robot’s FRF. The FRF calculated using OMA is shown to be in good agreement with results obtained from impact hammer experiments.
Subject
  • Spectroscopy
  • Biomedical engineering
  • Robots
  • Control theory
  • Signal processing
  • Robotics
  • Words coined in the 1920s
  • Audio amplifier specifications
  • Czech words and phrases
  • Science in popular culture
  • Frequency-domain analysis
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