A Novel Methodology for the Characterization of Cutting Conditions in Turning Processes Using Machine Learning Models and Acoustic Emission Signals

  1. de Barrena, Telmo Fernández 1
  2. Ferrando, Juan Luis 1
  3. García, Ander 1
  4. Arrazola, Pedro Jose 3
  5. Abete, Jose Manuel 3
  6. Herrero, Diego 2
  1. 1 Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009, Donostia-San Sebastian, Spain
  2. 2 Sidenor I+D, Barrio Ugarte s/n, 48970, Basauri, Spain
  3. 3 Faculty of Engineering, Mondragon University, 20500, Mondragon, Spain
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Actas:
16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021)

ISSN: 2194-5357,  2194-5365

ISBN: 9783030878689,  9783030878696

Año de publicación: 2021

Páginas: 556-566

Tipo: Aportación congreso

DOI: 10.1007/978-3-030-87869-6_53 GOOGLE SCHOLAR lock_openAcceso abierto editor

Objetivos de desarrollo sostenible

Resumen

In the last few years, the industry requires to know in real-time the condition of their assets. Acoustic Emission (AE) technique has been widely used to understand the real-time condition of manufacturing processes such as the degree of tool wear or tool breakage. Traditionally, to fulfil that goal, the information extracted from the signal sensors of the machines has been processed with mathematical models. This methodology is changing, and instead of developing complex physical models (where an in-depth knowledge of the system being modelled is required), the current trend is to use Machine Learning (ML) models which are based on previous data . Signal pre-processing and feature extraction is a complex task that usually generates a high amount of predicting variables. Therefore, this paper proposes a methodology to identify the best pre-processing tools, AE features and ML models to characterize cutting condition processes. This methodology is validated identifying cutting conditions in a turning process based on AE signals. To classify the cutting condition with the highest accuracy, several techniques are applied, (including wavelet transform for multiresolution analysis, Recursive Feature Elimination (RFE) technique, different classifiers (Decision Tree (DT), Random Forests (RF), Support Vector Machine (SVM), Gaussian Process (GP), K-Nearest Neighbor (KNN) and Multilayer Perceptron (MLP) classifiers) and different signal segmentation lengths. These techniques were evaluated using the data captured in a turning process when cutting a 19NiMoCr6 steel under pre-established cutting conditions. The best accuracy of predicting the cutting conditions based on AE signals was 99.7%, and it was achieved combining the wavelet packet transform (WPT) with RFE, with a segmentation time of 0.05 s and RF as classifier.

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Información de financiación

This research was partially funded by the department of industry of the Basque government within Elkartek programme (KK-2020/00099).

Financiadores

  • Eusko Jaurlaritza Spain
    • Materiales magnetoactivos multifuncionales para fabricación avanzada e industria inteligente

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