A Machine Learning Approach for Wear Monitoring of End Mill by Self-Powering Wireless Sensor Nodes

• Published in: Sensors (Basel, Switzerland) Switzerland), 2021-04, Vol.21 (9), p.3137
• Main Authors: Ostasevicius, Vytautas, Karpavicius, Paulius, Paulauskaite-Taraseviciene, Agne, Jurenas, Vytautas, Mystkowski, Arkadiusz, Cesnavicius, Ramunas, Kizauskiene, Laura
• Format: Article
• Language: English
• Subjects: Accuracy; Acoustics; Algorithms; Classification; Conical bodies; Costs; Data transmission; Electric pulses; End milling; Energy; Energy harvesting; Grooves; Machine learning; Monitoring systems; Neural networks; sensor node; Sensors; Signal processing; support vector machine (SVM); Support vector machines; Surface roughness; tool condition monitoring (TCM); Tool holders; tool vibrations; Tool wear; Vibration; Workpieces
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21093137

There are many tool condition monitoring solutions that use a variety of sensors. This paper presents a self-powering wireless sensor node for shank-type rotating tools and a method for real-time end mill wear monitoring. The novelty of the developed and patented sensor node is that the longitudinal oscillations, which directly affect the intensity of the energy harvesting, are significantly intensified due to the helical grooves cut onto the conical surface of the tool holder horn. A wireless transmission of electrical impulses from the capacitor is proposed, where the collected electrical energy is charged and discharged when a defined potential is reached. The frequency of the discharge pulses is directly proportional to the wear level of the tool and, at the same time, to the surface roughness of the workpiece. By employing these measures, we investigate the support vector machine (SVM) approach for wear level prediction.