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Published: 2004

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The ultimate goal in the manufacturing of a part is to achieve an economic production plan with precision and accuracy in the first attempt at machining. A physics-based comprehensive modeling of the machining processes is a fundamental requirement in identifying optimal cutting conditions which result in high productivity rates without violating accuracy throughout the part production process. This thesis presents generalized virtual simulation and optimization strategies to predict and optimize performance of milling processes up to 3-axis. Computationally efficient mathematical models are introduced 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 feedrate. Cutters with arbitrary geometries are modeled parametrically, and the intersection of helical cutting edges with workpiece features are evaluated either analytically or numerically depending on geometric complexity. The dynamics of generalized milling operations are modeled and the stability of the process is predicted using both time and frequency domain based models. These algorithms enable rapid simulation of milling operations in a virtual environment as the part features vary. In an effort to reduce machining time, a constraint-based optimization scheme is proposed to maximize the material removal rate by optimally selecting the depth of cut, width of cut, spindle speed and feedrate. A variety of user defined constraints such as maximum tool deflection, torque/power demand, and chatter stability are taken into consideration. Two alternative optimization strategies are presented: pre-process optimization provides allowable depth and width of cut during part programming at the computer aided manufacturing stage using chatter constraint, whereas the post-process optimization tunes only feedrate and spindl.