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Comparative analysis of the applicability of feed-forward and recurrent neural networks for prediction of surface quality of laser polished surfaces

DOITrouver le DOI : https://doi.org/10.2961/jlmn.2022.02.2007
AuteurRechercher : 1; Rechercher : 1; Rechercher : 1
Affiliation
  1. Conseil national de recherches Canada. Automobile et les transports de surface
FormatTexte, Article
Sujetlaser polishing; surface quality prediction; artificial intelligence; feed-forward neural network; recurrent neural network
Résumé
Laser polishing (LP) is an emerging manufacturing process capable to address some of the significant limitations of traditional surface quality improvement technologies such as abrasive or chemical based polishing processes. By reconfiguring the topography of the outer surface, surface characteristics such as quality, aesthetics, wettability, friction, bio-fouling resistance, and others can be enhanced at a relatively low cost. However, numerous LP process parameters have to be fine-tuned to achieve the intended surface quality. This makes the selection of optimal process parameters time consuming and often unrepeatable. This study suggests that while both feed-forward and recurrent neural networks can be used to predict LP surface quality with a reasonable accuracy, the latter is characterized by a superior performance.
Date de publication
Maison d’éditionJapan Laser Processing Society
Dans
  • Journal of Laser Micro/Nanoengineering 17, nº 2 (octobre 2022) : 116–120.
Langueanglais
Publications évaluées par des pairsOui
Exporter la noticeExporter en format RIS
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Identificateur de l’enregistrementef5b750d-837f-4036-a681-7d25ed4d9fd2
Enregistrement créé2024-02-09
Enregistrement modifié2024-02-09

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