Neural networks with fuzzy Petri nets for modeling a machining process

Moheb Maurice Hanna

doi:10.1117/12.304810

25 March 1998 Neural networks with fuzzy Petri nets for modeling a machining process

Moheb Maurice Hanna

Proceedings Volume 3390, Applications and Science of Computational Intelligence; (1998) https://doi.org/10.1117/12.304810
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

The paper presents an intelligent architecture based a feedforward neural network with fuzzy Petri nets 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 neural network approach employed the selected input parameters which defined by the machine operator via the CNC code. 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. An aim of the proposed architecture is to model the demanded quality of surface roughness as high, medium or low.

Citation Download Citation

Moheb Maurice Hanna "Neural networks with fuzzy Petri nets for modeling a machining process", Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); https://doi.org/10.1117/12.304810

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