Monitorización de estado de la herramienta en mecanizado mediante redes neuronales residuales robustas

  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

Proceedings:
Congreso de Fabricación Avanzada y Máquinas-herramienta 2023 (23CMH). Donostia-San Sebastian, 25-27 de octubre, 2023

Publisher: Cluster for Advanced & Digital Manufacturing

Year of publication: 2023

Pages: 12

Type: Conference paper

GOOGLE SCHOLAR lock_openeBiltegia editor

Abstract

Tool condition monitoring (TCM) aims to improve process efficiency, quality, and tool maintenance costs by monitoring critical variables such as tool wear. This study proposes a Deep learning (DL) architecture based on process-informed robust residual networks (Robust-ResNet) to predict tool wear in milling processes using time series of internal signals from the computer numerical control. The Robust-ResNet architecture uses skipping connections to move through multiple layers, avoiding the vanishing gradient problem of other neural network algorithms. In addition, an evaluation is performed on adding process information as input to the architecture and an attention mechanism between skips to make more robust predictions. The proposed architecture is trained and tested on an open-access dataset of milling time series, specifically alternating and direct current signals, and corresponding tool wear values. The results of this study demonstrate the benefits of using deep learning techniques in predicting tool wear using internal signals. The implementation of the proposed architecture is expected to help reduce maintenance costs, improve product quality, and increase production efficiency in manufacturing processes.

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Funding information

Este proyecto ha recibido financiación del programa de investigación e innovación Horizon 2020 de la Unión Europea bajo el acuerdo de subvención Marie Skłodowska-Curie número 814078 y por el Departamento de Educación, Universidades e Investigación del Gobierno Vasco bajo los proyectos Ikerketa Taldeak (Grupo de Ingeniería de Software y Sistemas IT1519-22 y Grupo de investigación de Mecanizado de Alto Rendimiento IT1443-22).

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