Discrete modeling of sculptured surface machining for robust automatic feedrate selection

Traditional feedrate selection techniques currently used in three and five-axis CNC machining reduces milling efficiency. Manually estimated feedrates tend to be conservative and constant, greatly increasing mill time. The goal of this research is to develop robust techniques and software tools for automatically generating optimized feedrates for use on three and five-axis CNC mills, to both simplify the feed selection process and to increase the safety and efficiency of the milling operation through milling process simulation. The simulation software estimates milling force vectors for each tool move, and identifies a feedrate that maintains a desired peak force. The desired cutting force value may be selected to prevent cutter breakage, maintain part tolerance, or meet some other criteria. Other conditions are also considered, such as maximum allowable chip thickness and machine constraints. This allows for the generation of variable feedrates that are optimized for each tool move. The software consists of three distinct portions: a discrete mechanistic model, a discrete geometric model, and a CNC machine model. The mechanistic model estimates cutting forces as a function of cut geometry, cutter/ stock relative velocity, and material constants. The geometric model keeps track of the changing in-process stock geometry and provides the cut geometry parameters required by the mechanistic model. The CNC machine model calculates the cutter/stock relative velocity based on feed inputs, machine kinematics, and controller behavior. A feed value is calculated in an iterative manner for each tool move based on the force estimates. The results of this research have produced accurate force estimates during sculptured surface machining, and have also demonstrated that this approach at automatic feedrate selection is feasible. Testing of feedrate selection has included the five-axis milling of production turbomachinery in an industrial environment. An average improvement in efficiency of 20% has resulted from the use of the optimized feeds.