A meta-learning strategy based on deep ensemble learning for tool condition monitoring of machining processes

  1. José Joaquín Peralta Abadía 1
  2. Mikel Cuesta Zabaljauregui 1
  3. Felix Larrinaga Barrenechea 1
  1. 1 Universidad de Mondragón/Mondragon Unibertsitatea
    info

    Universidad de Mondragón/Mondragon Unibertsitatea

    Mondragón, España

    ROR https://ror.org/00wvqgd19

Actas:
17th CIRP Conference on Intelligent Computation in Manufacturing Engineering

Editorial: Elsevier

Año de publicación: 2023

Páginas: 1-6

Congreso: 17th CIRP Conference on Intelligent Computation in Manufacturing Engineering Gulf of Naples (Italy) 12-14 July,

Tipo: Aportación congreso

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Resumen

For Industry 4.0, tool condition monitoring (TCM) of machining processes aims to increase process efficiency and quality and lower tool maintenance costs. To this end, TCM systems monitor variables of interest, such as tool wear. In this paper, a novel meta-learning strategy based on ensemble learning and deep learning (DL) is proposed for tool wear monitoring and is compared with state-of-the-art DL models selected from recent literature, using open-access datasets as input validating its implementation in an industrial scenario. As a result of this study, a novel meta-learning strategy for tool wear monitoring with minimum error is proposed and validated.

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