Identification of tool wear using acoustic emission signal and machine learning methods

• Published in: Precision engineering Vol. 72; pp. 738 - 744
• Main Authors: Twardowski, Paweł, Tabaszewski, Maciej, Wiciak – Pikuła, Martyna, Felusiak-Czyryca, Agata
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.11.2021
• Subjects: Acoustic emission; End milling; Machine learning; Tool wear; Acoustic emission; End milling; Tool wear; Machine learning

The work concerns the monitoring of the edge condition based on acoustic emission (AE) signals. The tool edge condition was determined by the wear width on the flank face. The processed material was an aluminum-ceramic composite containing 10% SiC. A carbide milling cutter with a diamond coating was...