Sensorless Classification of Severe Tool Wear in CNC Milling: Congratulations to Sandro Lic
Sandro Lic, Reseracher in Area Orchestration, has successfully defended his master’s thesis, “Sensorless Classification of Severe Tool Wear in CNC Milling using ResNet,” completed at Johannes Kepler University Linz. His research addresses the high costs and complexities of traditional tool condition monitoring by proposing a fully sensorless approach that leverages existing internal machine data, specifically …