An intelligent modeling system to improve the machining process quality in CNC machine tools using adaptive fuzzy Petri nets

The paper first presents an AND/OR nets approach for planning of a computer numerical control (CNC) machining operation and then describes how an adaptive fuzzy Petri nets (AFPNs) can be used to model and control all activities and events within CNC machine tools. It also demonstrates how product quality specification such as surface roughness and machining process quality can be controlled by utilizing AFPNs. The paper presents an intelligent control architecture based on AFPNs with learning capability for modeling a CNC machining operation and control of machining process quality. In this paper it will be shown that several ideas and approaches proposed in the field of robotic assembly are applicable to the planning procedure modeling with minor modifications. Graph theories, Petri nets, and fuzzy logic are powerful tools which are employed in this research to model different feasible states for performing a process and to obtain the best process performance path using exertion of the process designer's criteria.