Sensorless monitoring of cutting force variation with fractured tool under heavy cutting condition

Cutting force is widely regarded as being the one of the most valuable information for tool condition monitoring. Considering sustainability, sensorless cutting force monitoring technique using inner information of machine tool attracts attention. Cutting force estimation based on motor current is one of the example, and it is applicable to detection of tool breakage with some signal processing technique. However, current signal could not capture fast variation of cutting force. By improving monitoring performance of cutting force, the hidden tool condition information is more accessible. In this study, monitoring performance of cutting force variation due to tool fracture was enhanced by using multiencoder- based disturbance observer (MEDOB) and simple moving average. Friction force and torque which deteriorate monitoring performance was eliminated by moving average. First, monitoring accuracy of cutting force was verified through end milling test. Next, local peak value of estimated cutting force was extracted and the ratio of neighboring peak value was calculated to capture the tool fracture. Estimated value using MEDOB could capture the variation resulting from tool fracture.