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
• ISSN: 0141-6359; 1873-2372
• DOI: 10.1016/j.precisioneng.2021.07.019

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 used as the tool. Based on the AE signals, appropriate measures were developed that were correlated with the edge condition. Machine learning methods were used to assess the milling cutter's degree of wear based on AE signals. The applied approach using a decision tree allowed the prediction error of the tool condition class with a value below 6%. The method was also compared with other machine learning methods such as neural networks and the k-nearest neighbor algorithm. •Tool condition identification based on acoustic emission spectra•Machine learning methods for diagnostic decision system.•Identification of usability/unsuitability of end mills.•Easy-to-build system for identifying the condition of the cutting tool based on AE measurement [1–25].•The decision trees methods like diagnostic supervision system.