Application of a neural network for improving the quality of five-axis machining

As many operating parameters are involved in five-axis machining and significantly affect the machining results, use of either an analytical method or an experimental method can only solve the problem to a degree. Therefore, a concept for developing a database system using a neural network as the inference mechanism for the purpose of improving the quality of five-axis machining was addressed in this research. A database system was constructed on the basis of the VERICUT cutting simulation and experimental data from previous literature. The simulation data were put into an intelligent database for automatic generation of the suitable tool inclination angle for each cutting point on the machined surface. The tool inclination angle was the dominating factor for improving the surface quality in five-axis machining. The inclination angle was further entered into a post-processor to modify the cutter location file and to generate the numerical control (NC) program file for practical machining. The input data used in the database required for this system are basic operating parameters, quality requirements and the geometry of the designed sculptured surface. In this research, an intelligent database system for ball mills was successfully established for five-axis machining.