Knowledge-based approach for improvement of CNC part programs

Abstract In typical CNC machining processes, a CNC program specifies machining parameters and geometrical data. Part programmers usually write CNC programs based on their experience and perception. Because humans may not be able to sense physical quantities with high accuracy, modern CNC machines are equipped with a variety of sensors to indicate machining conditions. But modern sensing systems and human expertise are separated in most existing systems. Run-time information does not contribute to CNC program quality. This paper attempts to illustrate the structure for coordinating sensing data and expert knowledge to tune the CNC program. The turning process is done through both knowledge reasoning and mathematical processing. Simulation demonstrates convergent results of tuned feed rate under the tool deflection constraint.