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Veuillez utiliser cette adresse pour citer ce document : http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/13719

Titre: Computer numerical control machine tool wear monitoring through a data-driven approach
Auteur(s): Gougam, Fawzi
Afia, Adel
Ait Chikh, Mohamed Abdessamed
Touzout, Walid
Rahmoune, Chemseddine
Benazzouz, Djamel
Mots-clés: CNC machines
Machine learning
OSVR
Predictive maintenance
Tool wear monitoring
Date de publication: 2024
Editeur: SAGE
Collection/Numéro: Advances in Mechanical Engineering/ Vol. 16, N° 2(2024);pp. 1-5
Résumé: The susceptibility of tools in Computer Numerical Control (CNC) machines makes them the most vulnerable elements in milling processes. The final product quality and the operations safety are directly influenced by the wear condition. To address this issue, the present paper introduces a hybrid approach incorporating feature extraction and optimized machine learning algorithms for tool wear prediction. The approach involves extracting a set of features from time-series signals obtained during the milling processes. These features allow the capture of valuable characteristics relating to the dynamic signal behavior. Subsequently, a feature selection process is proposed, employing Relief and intersection feature ranks. This step automatically identifies and selects the most pertinent features. Finally, an optimized support vector machine for regression (OSVR) is employed to predict the evolution of wear in machining tool cuts. The proposed method’s effectiveness is validated from three milling tool wear experiments. This validation includes comparative results with the Linear Regression (LR), Convolutional Neural Network (CNN), CNN-ResNet50, and Support Vector Regression (SVR) methods
URI/URL: https://journals.sagepub.com/doi/10.1177/16878132241229314
https://doi.org/10.1177/16878132241229314
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/13719
ISSN: 1687-8132
Collection(s) :Publications Internationales

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