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

The paper presents an intelligent architecture based fuzzy Petri nets with a feedforward neural network for modeling product quality in a CNC machining center. It discusses how the proposed architecture can be used for modeling, monitoring and control a product quality specification such as surface roughness. The surface roughness represents the output quality specification manufactured by a CNC machining center as a result of a milling process. The fuzzy Petri nets approach utilized the exact input milling parameters, such as spindle speed, feed-rate, tool diameter and coolant (off/on), which can be obtained via the machine or sensors system. The neural network approach employed the selected input parameters which defined by the machine operator via the CNC code. The aim of the proposed architecture is to model the required quality of surface roughness