Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

27-10-2021 | Machine Tool

Tool wear and remaining useful life (RUL) prediction based on reduced feature set and Bayesian hyperparameter optimization

Journal:
Production Engineering
Authors:
Fabio C. Zegarra, Juan Vargas-Machuca, Alberto M. Coronado
Important notes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Accurate prediction of machine tool wear is an essential part of modern and efficient manufacturing. In recent years, many studies have been carried out using machine learning algorithms, both traditional and deep learning; with the latter ones reporting the highest precisions. The present work aims to show that, in the tool wear prediction problem, traditional methods can have a performance similar to the state of the art, obtained using deep learning methods. The data used here is presented in the form of time series, which cannot be used directly by traditional machine learning algorithms, such as the ones used in this work. To link the raw data and the learning algorithm, it is first necessary to extract a set of features from the time series. In addition, some preprocessing techniques, Bayesian hyperparameter optimization and forward feature selection are applied. In this work, two freely accessible databases are used with two different but related objectives, the first is used to predict machine tool wear, while the second is used to predict the remaining useful life of machine tools. For the first case, errors (RMSE) of less than 10 were obtained, while in the second case scores above 85% were achieved. In both cases, these results are comparable to the state of the art. Using the methodology presented here makes it possible to obtain very accurate tool wear predictions at a lower computational cost, both due to the use of less complex models and to a reduced set of features.

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 30 Tage kostenlos.

Literature
About this article

Premium Partners

    Image Credits