An adaptive control optimization system using a model to represent actual physical phenomena in milling is discussed. The model is used for the identification of physical parameters, the calculation of the temperature at the tool edges, and the estimation of the tool wear rate. The shear angle of the shear plane, the flank wear land length of the tool edge, the true contact area at the flank wear land, the radial depth and the axial depth of cut are identified as the physical parameters, the shear stress, and the hardness of the work material from bending moments and torque in the spindle generated by the cutting force. The temperature at the flank wear land is calculated from identified parameters. The tool wear is represented theoretically as the summation of the thermal, mechanical and shock wears. Each wear is calculated from identified parameters and the temperature at the tool edges. Adaptive control experiments to keep the tool-wear rate at a constant value verify that the total system works well. An adaptive control optimization system using the tool-wear rate equation is compared with an adaptive control constraint system using Taylor’s tool life equation in a computer simulation. The simulation shows that adaptive control optimization gives higher cost efficiency than adaptive control constraint when the process parameters vary.

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