The invention discloses a
numerical control machine tool cutter
wear loss online prediction method based on spindle currents and vibration signals. The method is characterized in that multiple cuttersof the same model are utilized to repeatedly carry out trial operation
machining under the same working condition on a
numerical control machine tool spindle provided with a sensor, and original
machining wear data are measured; according to three wear stages of the initial rapid wear stage, the normal wear stage and the rapid wear stage of the cutters, an optimal
feature set for training is extracted from each wear stage, and a
support vector regression machine prediction model is trained; and finally the trained model is adopted for online real-time prediction of cutter
wear loss in the actual
machining process. According to the method, through the data obtained by repeated experiments through the multiple of cutters of the same model under the same condition, characteristic parameters,related to cutter wear, in the wear stages in original signals can be fully mined, the relational degree between the characteristics and the cutter
wear loss is enhanced through a post-
processing mode, and therefore, the constructed
support vector regression machine prediction model can obtain high prediction precision and good generalization performance.