FUZZY PETRI NETS WITH NEURAL NETWORKS TO MODEL PRODUCTS QUALITY FROM A CNC-MILLING MACHINING CENTER

This paper presents a Petri net approach for the modeling of a CNC-mil ling machining centre, Next, by utilizing fuzzy logic with Petri nets (fuzzy Petri nets), a technique based on 9 fuzzy rules is developed, T his paper demonstrates how fuzzy input variables, fuzzy marking, fuzzy firing sequences, and a global output variable should be defined for use with fuzzy Petri nets, The technique employs two fuzzy input varia bles (spindle speed and feed rate), throughout the milling operation i n order to determine surface roughness, Additionally, a fuzzy Petri ne t is used with an artificial neural network for the modeling and contr ol of surface roughness, Experimental results illustrate that the tech nique developed can be of benefit when the cutting tool has suffered d amage throughout the milling operation, It also shows how the techniqu e can react when the quality is high, medium, or low, The surface roug hness represents the quality specification of products from the CNC-mi lling machining centre.