| DOI | Trouver le DOI : https://doi.org/10.1016/j.cirp.2021.03.024 |
|---|
| Auteur | Rechercher : Hassan, M.1Identifiant ORCID : https://orcid.org/0000-0001-6881-3882; Rechercher : Sadek, A.1; Rechercher : Attia, M. H. |
|---|
| Affiliation | - Conseil national de recherches du Canada. Aérospatiale
|
|---|
| Bailleur de fonds | Rechercher : National Research Council of Canada |
|---|
| Format | Texte, Article |
|---|
| Sujet | cutting; machine learning; condition monitoring |
|---|
| Résumé | A sensor-based hybrid processing approach for tool wear monitoring is presented to overcome the practical limitations of implementing state-of-the-art tool condition monitoring systems in milling processes. It extracts features from vibration signals that are insensitive to the variations in cutting conditions, tool path and interfering noises. A machine learning model was developed to accentuate features separation based on tool condition. Extensive experimental validation tests in high speed and conventional milling applications demonstrated the approach capability to achieve 98% accuracy and reduce system training by up to 97%. Such performance, practicality and accuracy have never been reached before in this application. |
|---|
| Date de publication | 2021-04-20 |
|---|
| Maison d’édition | Elsevier |
|---|
| Dans | |
|---|
| Langue | anglais |
|---|
| Publications évaluées par des pairs | Oui |
|---|
| Identificateur | S000785062100024X |
|---|
| Exporter la notice | Exporter en format RIS |
|---|
| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
|---|
| Identificateur de l’enregistrement | b846bab5-2c89-4768-91e5-5627bc3ef74e |
|---|
| Enregistrement créé | 2023-02-21 |
|---|
| Enregistrement modifié | 2023-03-16 |
|---|
