This work describes a fuzzy expert system for rough turning. In order to automate unmanned turning, safety of the process must be ensured. In addition, any quality requirements should be fulfilled and, within these constraints, productivity maximized. The traditional approach in adaptive control of machining is to keep a measured quantity, such as power, within acceptable limits. However, there have been some studies measuring distinct phenomena in machining and identifying “cutting states” based on the phenomena. By identifying cutting states corresponding to phenomena monitored by human experts, it is possible to construct an intelligent machining system emulating the decision making of a human expert. This paper concentrates on defining the requirements for the inference part of such of an intelligent machining system. This work concentrates on both functional requirements, such as capability to take into account specific cutting states. The existence of process monitoring subsystems which detect and measure the cutting phenomena is assumed. As a result, a Sugeno-type fuzzy control is suggested, and feasibility and the level of completeness of such a system are discussed and issues requiring further study are identified.
Intelligent Machining System Based on CNC Controller Parameter Selection and Optimization
