Model-based Learning Control of Cutting Forces in End Milling Processes
Soichi Ibaraki (Kyoto University)
Takashi Ogawa (Denso Corp.)
Atsushi Matsubara (Kyoto University)
Yoshiaki Kakino (Kyoto University)
In order to fully utilize the potential of high-speed machining centers for high productive machining, the optimal process design is a critical issue. In particular, this paper considers the feedrate optimization in end milling processes based on the cutting force control. Compared to feedback control approaches that are often found in the literature, a model-based feedforward control approach is simpler, but has an inherent advantage especially when the process is subject to quick change. This paper proposes an iterative model updating method, which allows the model-based feedforward approach to have the adaptability to unmodelled processes to some extent. Starting from the initial model, which is provided from the database, the process model is updated at each machining cycle, and consequently, the control law is improved in an iterative learning manner. Taking a pocket machining as an example, the proposed scheme is implemented within the following three canned milling cycles: 1) corner rounding, 2) internal cylindrical machining by spiral cycles, and 3) slot milling by trochoid cycles. The practical applicability and effectiveness of the proposed approach are verified in experimentation.
Keywords:   Cutting force control, milling processes, model-based feedforward control, iterative learning, canned cycles.