The hairpin motor is among the motors used in eco-friendly automobiles. Unlike the random-winding method, this method features a square cross-sectional enamel coil formed into several hundred hairpins inserted into the stator. This offers an increased space factor, which leads to enhanced motor power. With the growing use of hairpin motors, there is an increasing demand for automated equipment in their production. With an aim to improve the manufacturing quality of hairpin motors, many countries are conducting research and development. The process of enamel removal is one of the key steps in hairpin-forming equipment and can significantly affect the performance of the motor. A precise machining process technology is required to improve the enamel removal process, achieve higher enamel removal rates, and reduce wire loss rates. Grinding is a technique used for enamel removal. In this study, an adaptive control algorithm was applied to the grinding process to enhance the enamel removal machining performance. During the machining process, the vertical position of the spindle was controlled to maintain a consistent machining depth for tracking the target grinding force. An experimental setup, which included a system for measuring the spindle torque to calculate the grinding force in real time, was created. To validate the performance of the adaptive control technology, experiments were conducted using this setup. Furthermore, experiments were conducted to observe the changes in the grinding force ratio based on the conditions of the grinding wheel. The correlation between the grinding force ratio and tool condition was analyzed, and based on this, the feasibility of assessing tool condition and determining the timing for tool replacement through real-time monitoring of the grinding force ratio was confirmed.