Research on online identification of surface burnishing tool machining conditions by spindle current signal analysis

Abstract

The surface burnishing tool (SBT) is optimized for aluminum alloys' surface hardness and surface roughness. The SBT's rolling body wears during machining and reaches a certain threshold where the machining performance decreases. Sandpapers are used to abrade the rolling bodies to simulate surface damage. The preconditioned tools are utilized for machining, and the spindle motor current signals are recorded. The SBT's hardening capacity is weakened when the rolling body's Sa exceeds 0.581, and the finishing capacity is weakened when the Sa exceeds 0.684. The PSO-SVM model accurately identifies the failure point of the SBT's hardening capacity with an accuracy of 96.67%. Another PSO-SVM model accurately identified the failure point of SBT's finishing capacity with an accuracy of 85.83%.