Efficient Prediction of Long-Term Thermal Trends and Peak Temperatures in Milling using a Hybrid Adaptive-Superposition Approach

In milling processes, the permissible cutting speed is significantly constrained by transient temperature spikes at the cutting edge, which drive wear mechanisms. However, predicting these peaks over realistic process durations presents a computational bottleneck. Resolving high-frequency events requires small steps, while capturing the global heat accumulation requires simulating long durations. Existing approaches often face a trade-off between computational cost and physical fidelity, leading to uncertainty regarding the validity of simplified fast-prediction models. This study introduces a computationally efficient 3D heat-conduction framework to resolve this conflict. Utilizing an adaptive time-stepping scheme, the model bridges the gap between millisecond-scale engagement flashes and minute-scale global heating with high geometric accuracy. Due to its rigorous spatial and temporal discretization, this framework serves as a high-fidelity reference to quantify the impact of environmental boundary conditions. The analysis reveals that peak temperatures are largely dominated by the heat input, while environmental cooling mechanisms, including convection and radiation, play a minor role during the initial process phase. Crucially, the adaptive framework is utilized to benchmark an efficient superposition strategy. The comparative analysis reveals that for standard solid carbide tools under dry cutting conditions (Bi ≪ 1), the linear superposition of a global base temperature and a local transient flash provides an accurate approximation of the maximum tool temperature. Consequently, while the adaptive FEM is essential for validating complex boundary scenarios, the superposition strategy is identified as the robust, fast solution for industrial process dimensioning.