This paper aims to establish a milling temperature model of the spinal lamina, which mainly considers the influence of bone density and milling parameters ( bone cutting depth and milling feed speed) on the milling temperature. Firstly, based on the analysis of the lamina′s layer-by-layer bone-cutting process, a series of bone-cutting experiments with different parameters are designed by the full factor experiment method, and the emissivity of the cancellous bone materials is calibrated. Then, the temperature data needed to establish and validate the model are collected by cutting layers of artificial cancellous bone materials with different densities at different depths and feed rates using a robot and an orthopedic ball-end milling cutter. A thermal imaging camera measures two kinds of milling temperatures during the process of the layer-by-layer cutting by the robot, which are the temperature of the milling cutter and the temperature of the bone surface. Finally, the influence of the bone density and milling motion parameters on these two types of milling temperatures is analyzed, and a prediction model of lamina′s cancellous bone milling temperature is established using experimental data and a neural network. Experimental results show that the goodness of fit between the temperature value estimated by the model and the measured value of the bone cutting experiment is 0. 97. The proposed model can help surgeons or robots to select appropriate milling motion parameters when milling cancellous bone with different densities layer by layer to improve the safety of laminectomy.