The ultimate aim of the manufacturing is to produce the first part correctly and most economically on the production floor. This paper presents computationally efficient mathematical models to predict milling process state variables, such as chip load, force, torque, and cutting edge engagement at discrete cutter locations. Process states are expressed explicitly as a function of helical cutting edge-part engagement, cutting coefficient, and feed rate. Cutters with arbitrary geometry are modeled parametrically, and the intersection of their helical cutting edges with workpiece features are evaluated either analytically or numerically depending on the geometric complexity. Process variables are computed for each cutting edge-part engagement feature and summed to predict the total force, torque, and power generated at each feed rate interval. The proposed algorithms are experimentally verified in simulating milling of a gear box cover, and integrated to the virtual milling process system, which is capable of predicting cutting forces, torque, power, and vibrations within CAM environment.

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