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Titre: Diagnosis and prognosis of cutting tools based on blind sources separation : application to milling
Auteur(s): Bazi, Rabah
Rechak, Saïd, Directeur de thèse
Benkedjouh, Tarak, Directeur de thèse
Mots-clés: Tool Wear Monitoring
Data Extraction and Reduction
Health Indicator
Remaining useful Life
Deep Learning
Extreme Learning
Date de publication: 2022
Résumé: In this study we have developed two new models for the automatic monitoring of the state of health of cutting tools. This method consists of estimating; validate and calculate their residual life (RUL). The method uses monitoring data provided by sensors (Force, Acceleration AND AE), and is based on two main stages: the first stage online and the second stage offline. In the first model, the raw signals provided by the sensors are first processed to extract useful information through the use of continuous wavelet transform (CWT), source separation "SCA" and the Tagichi-Mahal system. -Anubis (MTS); and in the second model, variational mode decomposition (VMD) and (CNN-BLSTM) were applied. Data processing after source separation is the subject of the development of the health indicator. The prediction of the residual life allows the determination of the accuracy and the various parameters of the prognosis like the RMSE, which allow to classify and validate the robustness of this model. The proposed method can be implemented on real data in order to determine the wear of the cutting tools during the machining process of the different materials.
Description: Thèse de Doctorat : Génie Mécanique : Alger, École Nationale Polytechnique : 2022
Collection(s) :Département Génie Mécanique

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