| DOI | Resolve DOI: https://doi.org/10.1016/j.cirp.2021.03.024 |
|---|
| Author | Search for: Hassan, M.1ORCID identifier: https://orcid.org/0000-0001-6881-3882; Search for: Sadek, A.1; Search for: Attia, M. H. |
|---|
| Affiliation | - National Research Council of Canada. Aerospace
|
|---|
| Funder | Search for: National Research Council of Canada |
|---|
| Format | Text, Article |
|---|
| Subject | cutting; machine learning; condition monitoring |
|---|
| Abstract | 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. |
|---|
| Publication date | 2021-04-20 |
|---|
| Publisher | Elsevier |
|---|
| In | |
|---|
| Language | English |
|---|
| Peer reviewed | Yes |
|---|
| Identifier | S000785062100024X |
|---|
| Export citation | Export as RIS |
|---|
| Report a correction | Report a correction (opens in a new tab) |
|---|
| Record identifier | b846bab5-2c89-4768-91e5-5627bc3ef74e |
|---|
| Record created | 2023-02-21 |
|---|
| Record modified | 2023-03-16 |
|---|
