A perturbation signal based data-driven Gaussian process regression model for in-process part quality prediction in robotic countersinking operations

Leco, M. and Kadirkamanathan, V. (2021) A perturbation signal based data-driven Gaussian process regression model for in-process part quality prediction in robotic countersinking operations. Robotics and Computer-Integrated Manufacturing, 71. 102105. ISSN 0736-5845

Abstract

Metadata

Authors/Creators:
  • Leco, M.
  • Kadirkamanathan, V.
Copyright, Publisher and Additional Information: © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Robotic machining; Process monitoring; Gaussian process regression; Predictive models; Signal processing
Dates:
  • Accepted: 1 December 2020
  • Published (online): 21 March 2021
  • Published: October 2021
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Funding Information:
FunderGrant number
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCILEP/P006930/1
Depositing User: Symplectic Sheffield
Date Deposited: 26 Mar 2021 16:27
Last Modified: 26 Mar 2021 16:27
Status: Published
Publisher: Elsevier BV
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.rcim.2020.102105

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