Learning Approach to Cycle Time Minimization of Wood Milling Using Adaptive Force Control
A majority of the machining processes in the industry of today is performed using position controlled machine tools, where conservative feed rates have to be used in order to avoid excessive process forces. By instead controlling the process forces, the feed rate, and consequently the material removal rate, can be maximized. In turn, this leads to decreased cycle times and cost savings. Furthermore, path planning with respect to time minimization for milling processes, especially in non isotropic materials, is not straightforward. This paper presents a model based adaptive force controller that achieves optimal feed rates, in combination with a learning algorithm to obtain the optimal machining path, in terms of minimizing the milling duration. The proposed solution is evaluated in both simulation and experiments, where an industrial robot is used to perform rough cut wood milling. Cycle time reductions of 14% using force control compared to position control were achieved, and on average an additional 28% cycle time reduction with the proposed learning algorithm.