An intelligent modular modelling approach for quality control of CNC machines product using adaptive fuzzy Petri nets

The paper first presents an AND/OR nets approach for planning of a 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 utilising AFPNs. Utilising fuzzy Petri nets (FPN), a technique based on nine weighted fuzzy rules is developed. The machine tool vibration (V), cutting force (F), spindle speed (S) and feed rate (f) throughout the machining operation are used to determine surface roughness (R). Then machining time (t) and surface roughness (R) are used in order to specify the machining process quality (Q). Next, control architecture model of fuzzy rule-based expert systems is shown with FPN. At the end of paper, a case study review for the application of AFPNs to a product manufacturing by a CNC machine.