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Modeling product quality from a flexible machining center using fuzzy Petri nets with neural networks

In this paper, an intelligent control architecture has been proposed to model, monitor, and control the product quality in a CNC milling machining center. The adopted architecture uses a fuzzy timed Petri net (fuzzy logic with timed Petri nets) and synergistic neural networks. The paper demonstrates how fuzzy input variables, fuzzy marking, fuzzy time-firing sequences, fuzzy outputs reasoning, and a global output variable should be defined for use with fuzzy timed Petri nets. The technique employed with the fuzzy timed Petri net uses two fuzzy input variables, spindle speed, and feed rate, through the milling process in order to maintain product quality characteristics such as surface finish. It also illustrates how the technique reacts when the product quality is high, medium, or low.