TOOL CONDITION MONITORING AND TOOL DEFECT DETECTION FOR END MILLS BASED ON HIGH-FREQUENCY MACHINE TOOL DATA

Abstract

In the context of increasing digitalization, machine tools have a decisive impact on the manufacturing of technically sophisticated products. The resulting large amount of available data opens up new opportunities for process monitoring and optimization. In this paper, a new in-process tool condition monitoring (TCM) approach for end mills is developed. Besides in-process wear determination, the presented approach also enables the early detection of tool manufacturing defects on end mills. By applying machine learning algorithms, high prediction accuracies can be achieved. The results allow the implementation of an in-process TCM system based on internal machine tool data.

Recommended articles

WELDABILITY OF COBALT ALLOYS BY HYBRID METHODS

TOMAS HERCIK, MARIAN SIGMUND, PETR HRUBY
Keywords: Cobalt | cobalt alloys | Stellite | unconventional welding | conventional welding

FRAMEWORK FOR COUPLED DIGITAL TWINS IN DIGITAL MACHINING

D. Plakhotnik, A. Curutiu, A. Zhulavskyi, Xavier Beudaert, Jokin Munoa, M. Stautner
Keywords: CAM | digital twin | digital machining | NC verification

IDENTIFICATION OF THE ROOT CAUSE OF SURFACE TOPOGRAPHY INACCURACIES BY MEANS OF PROCESS MONITORING: INDUSTRIAL EXAMPLES

M. Gil, X. Beudaert, J. Munoa, D. Barrenetexea, J.A. Sanchez
Keywords: chatter | Defects | surface topography | vibrations

DEVELOPMENT OF A DECISION BOX OF DIAGNOSTIC SYSTEM FOR ELECTRIC DRIVES

PAVEL STEPANOV, PAVOL BOZEK, STANISLAV LAGUTKIN, YURY NIKITIN
Keywords: Current | vibration | neural network | diagnostics | electric drive