Temperature Field Prediction of Variable Bone Milling Conditions Based on Thermal-Parameter Calibration and Finite-Element Simulation

Bone milling is a common surgical procedure widely used in dental, orthopedic, and plastic surgeries. Bone has low thermal conductivity and high specific heat. As a result, significant heat generated during bone milling tends to accumulate in the milling area, often leading to thermal necrosis. Currently, there are no suitable sensors available to monitor the temperature during bone milling, which forces surgeons to rely on their personal experience to determine if the temperature is too high. This article presented a temperature field prediction model for robot-assisted bone milling using finite-element techniques. This model can predict the temperature distribution under various milling conditions, providing a valuable reference for surgeons. In developing this model, the thermal parameters of the cortical bone and the convective heat transfer coefficient of the environment were unknown. In order to determine these parameters, a heat conduction experiment based on a point heat source was designed. The identified parameters were then used to build the temperature prediction model. Five sets of dry milling experiments with different milling angles and depths and one set of experiments with irrigation fluid added were performed using a 4-DOF robot to verify the model. The maximum mean absolute errors (MAEs) between the predicted data and the experimental data from these six experiments were $0.409~^{\circ }$ C, $0.458~^{\circ }$ C, $0.514~^{\circ }$ C, $0.601~^{\circ }$ C, $0.561~^{\circ }$ C, and $0.619~^{\circ }$ C, respectively.