Cutting forces in the milling process vary depending on the work-piece geometry and cutting parameters. When the cutting forces exceed a certain limit, the tool may break and cause damage to the work-piece and eventually to the machine tool. Adaptive cutting force control systems can be used to manipulate cutting operation parameters in order to keep the cutting forces at a safe level. Successful application of the method leads to increased metal removal rate and productivity in machining processes.

In this thesis, a second order transfer function is used to represent the time invariant dynamics of a research milling machine's feed drive servo system. The command feed velocity is the input and the actual feed is the output of the servo system. The actual feed manipulates the cutting forces which are modelled by a first order time varying dynamic system.

Three existing adaptive control methods have been designed to control the milling process. Adaptive Proportional Integral Derivative (PID), Pole-Placement and Model Reference Adaptive Control (MRAC) algorithms have been simulated and experimentally verified. It has been shown that when the dynamics of both the time invariant servo and the time variant cutting process are modelled correctly, the adaptive control algorithms can perform well. Simulations and experiments, which have been carried out with identical cutting conditions, show that PID and Pole-Placement controllers can be successfully applied to milling force control.