LAUSR

This paper presents a new approach to detecting tool chipping by analyzing peak period of spindle vibration, for changing the tool securely before the occurrence of a tool fracture. Peak period analysis refers to the observation of time differences for adjacent peak values, which gives the quantitative variation in time domain. Peak period refers to the time difference for adjacent peak values and indicates changes of frequency component based on the tool condition, thus, it can be used to monitor tool condition. During the end-milling of Inconel 718, monitoring variables such as cutting force, spindle vibration, and spindle current are monitored with sensors. The correlations of monitoring variables are analyzed by comparing them from the perspective of machining pass and tooth period. The spindle vibration is used for detecting the tool chipping and then the peak periods at each machining pass are featured with a statistical value. The result is significantly successful for detecting tool chipping. As chipping occurs in the tool, the magnitude of the feature value increases significantly. Furthermore, the real-time chipping detection capabilities of the proposed method are tested.