Adaptive Fuzzy Neural Network Control System in Cylindrical Grinding Process

X.M. Li; Ning Ding

doi:10.4028/www.scientific.net/KEM.426-427.220

Paper Titles

A New Tracking Control Algorithm for Mobile Robots
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Research on Look-Ahead Adaptive Interpolation of NURBS Based on de Boor Algorithm
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Machining Mechanism of Abrasive Water Jet on Ceramics
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Study on ANFIS Application in Coal Mining Stray Current Security Prediction
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Adaptive Fuzzy Neural Network Control System in Cylindrical Grinding Process
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Experimental Investigation of Turning with Magnetized Cutting Emulsion
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Inverse Design of Cam Contour by NURBS Spline
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Research and Implement of Automated Eliminating Bubbles Method Oriented to Vacuum Casting
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Micro/Nano Functional Manufacturing: From Microthruster to Nano Energetic Material to Micro/Nano Initiator
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Adaptive Fuzzy Neural Network Control System in Cylindrical Grinding Process

Abstract:

An adaptive fuzzy neural network control system in cylindrical grinding process was proposed. In this system, the initial cylindrical grinding parameters were decided by the expert system based on fuzzy neural network. Multi-feed and setting overshoot optimization methods were also adopted during the grinding process, and a human machine cooperation system (composed of human and two fuzzy – neural networks) could revise the process parameters in real-time. The experiment of the cylindrical grinding was implemented. The results showed that this control system was valid, and could greatly improve the cylindrical grinding quality and machining efficiency.