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Optimal design for compliance modeling of industrial robots with bayesian inference of stiffnesses

Tepper, Cornelia ; Matei, Alexander ; Zarges, Jonas ; Ulbrich, Stefan ; Weigold, Matthias (2025)
Optimal design for compliance modeling of industrial robots with bayesian inference of stiffnesses.
In: Production Engineering : Research and Development, 2023, 17 (5)
doi: 10.26083/tuprints-00028395
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Optimal design for compliance modeling of industrial robots with bayesian inference of stiffnesses
Language: English
Date: 16 January 2025
Place of Publication: Darmstadt
Year of primary publication: October 2023
Place of primary publication: Berlin ; Heidelberg
Publisher: Springer
Journal or Publication Title: Production Engineering : Research and Development
Volume of the journal: 17
Issue Number: 5
DOI: 10.26083/tuprints-00028395
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

In this paper a cost and time efficient approach to setup a compliance model for industrial robots is presented. The compliance model is distinctly determined by the gear’s stiffness parameters which are tuned by an optimal design of experiments approach. The experimental setup consists of different poses of the robot’s axes together with the applied force at the tool center point (TCP). These robot poses represent together with defined forces the experimental setup where the deviation of the robot under defined force is measured. Based on measurements of the displacement of the TCP the stiffness parameters for the compliance model are estimated and afterwards validated in new experiments. The efficiency of this approach lies in the reduced amount of experiments that are needed to identify the stiffness parameters that are parameters inherent to the compliance and the less complex experimental setup.

Uncontrolled Keywords: Robots, Milling, Optimal design of experiments, Bayesian inference, Stiffness estimation
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-283951
Classification DDC: 500 Science and mathematics > 510 Mathematics
600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
600 Technology, medicine, applied sciences > 670 Manufacturing
Divisions: 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > TEC Manufacturing Technology
04 Department of Mathematics > Optimization > Nonlinear Optimization
Date Deposited: 16 Jan 2025 14:09
Last Modified: 16 Jan 2025 14:09
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/28395
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