The importance of cutting forces in machining has been emphasized for monitoring and optimizing cutting conditions, leading to various method to detecting cutting forces researched. Cutting forces can be directly measured using dynamometer or indirectly estimated using AE sensors and accelerometers, etc. However, these external sensors demand high costs and have accuracy limitations due to environment issues. To compensate for these drawbacks, utilizing internal signals of machine tool has been developed. Among these, using internal electrical signals of machine tool is representative. In commercial machine tools, cutting forces are often estimated through current measurements. However, due to the characteristics of the spindle motor, electrical properties such as slip, power factor, and efficiency vary with the load, resulting in relatively lower accuracy. This study introduces current-based method considering characteristics of motor and power-based method for estimating cutting forces and compare accuracy of those methods with the measurements from dynamometer respectively.

A hairpin motor is a type of motor that is used for driving an eco-friendly car. Unlike a conventional coil-winding motor, hundreds of hairpins formed by an enameled copper wire with a rectangular cross section comprise a stator to improve the driving efficiency by maximizing a coil drip rate. With the increased use of the hairpin motor, there has been an increased interest in manufacturing techniques and automated systems of the hairpin motor. Enamel coating removal is one of the major processes of hairpin motor production; enamel coating at the end of the hairpin should be removed to connect the hundreds of hairpins by using the welding process. Grinding is one of the machining processes used for removing the enamel coating. This study proposed an adaptive control method for the grinding process to improve the efficiency and quality of the enamel coating removal process. Grinding depth is maintained during machining by controlling the vertical position of the spindle based on driving torque. A lab-scale grinding machine including a sensory system for adaptive control is developed and used to verify the performance of the proposed method.