Prediction of bead geometry using a two-stage SVM–ANN algorithm for automated tungsten inert gas (TIG) welds

Kshirsagar, R., Jones, S., Lawrence, J. et al. (1 more author) (2019) Prediction of bead geometry using a two-stage SVM–ANN algorithm for automated tungsten inert gas (TIG) welds. Journal of Manufacturing and Materials Processing, 3 (2). 39. ISSN 2504-4494

Abstract

Metadata

Authors/Creators:
  • Kshirsagar, R.
  • Jones, S.
  • Lawrence, J.
  • Tabor, J.
Copyright, Publisher and Additional Information: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Keywords: bead geometry prediction; support vector machines; artificial neural networks; data classification
Dates:
  • Accepted: 5 May 2019
  • Published (online): 8 May 2019
  • Published: 8 May 2019
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Advanced Manufacturing Institute (Sheffield) > Nuclear Advanced Manufacturing Research Centre
Depositing User: Symplectic Sheffield
Date Deposited: 04 May 2020 11:33
Last Modified: 04 May 2020 11:33
Status: Published
Publisher: MDPI AG
Refereed: Yes
Identification Number: https://doi.org/10.3390/jmmp3020039

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