Machining, Measurement, and Control Laboratory

Model-Based Learning Control of Cutting Forces in End Milling Processes
Atsushi Matsubara (Kyoto University)
Soichi Ibaraki (Kyoto University)
Takashi Ogawa (Kyoto University)
Yoshiaki Kakino (Kyoto University)

To optimize the machining productivity without sacrificing the tool life, the optimal design of cutting conditions is a critical issue. In particular, the practical importance to control the cutting force in end milling processes has been widely recognized. This paper presents a model-based feedforward control scheme of cutting forces with an iterative learning algorithm. By using the initial process model provided by the database, the feedrate profile is optimized such that the cutting force is controlled at the desired level along the given tool path. The process model is updated at each machining cycle, and then the control performance is improved in an iterative learning manner. As a practical application of the cutting force control, the proposed approach will be implemented in canned milling cycles. By using the corner rounding as an application example, the practical applicability and effectiveness of the proposed approach are verified in experimentation.

Key words: NC Machine Tools, NC Servo System, Cutting Force, Adaptive Control.