Optimization of cutting fluid supply based on the motor current

A cutting fluid is commonly used in the milling process to improve the tool life and surface quality by cooling the cutting zone and decreasing the friction force. To precisely supply the cutting fluid to the cutting zone, the supply direction should be controlled properly by considering the feed direction and machining type. For example, in the side milling process, it is effective to inject the cutting fluid perpendicular to the feed direction, whereas, in the slot milling process, the backside of the feed direction is preferable. However, it is difficult to change the supply direction of the cutting fluid during the machining process because a hand-operated cutting fluid supplier is generally applied to conventional machine tools. The multi-directional injection of the cutting fluid by using several nozzles constitutes an alternative approach; however, this can lead to the increased consumption of energy during the supply and cooling. This paper presents an intelligent cutting fluid supplier that can find the optimal supply direction and control the nozzle position during the machining process. To control the nozzle position during machining, a three degrees-of-freedom (DOF) robot arm that can be attached to the spindle was constructed. The effect of the relative angle between the nozzle and feed direction on the surface quality was investigated experimentally in order to derive the optimal supply direction with respect to the feed direction. The optimal supply direction was detected in real-time based on the feed direction and machining type estimated by the cutting forces in the feed direction and perpendicular to the feed direction. A disturbance observer was designed to estimate the cutting force applied to the x- and y-axis based on the table position and torque command generated by the motion controller. A machining type detection algorithm is proposed to distinguish the machining types in real-time based on the cutting force. Slot milling and side milling experiments were conducted to demonstrate the feasibility of the proposed intelligent cutting fluid supplier. The proposed machining type detection algorithm achieved an accuracy of 93 % during the machining process.