Study of Intelligent Prediction Control of Surface Roughness in Grinding

Ning Ding; Long Shan Wang; Guang Fu Li

doi:10.4028/www.scientific.net/KEM.329.93

Paper Titles

Wear Characteristics of the Sub-Aperture Tools and Material Removal Rate in Precision Grinding with Loose Abrasives
p.69

Experimental Studies on Grinding of Titanium Alloy with SG Wheels
p.75

Inverse Method for Determining Grinding Area and Material Removal Amount in Grinding Radome
p.81

Study on The Self-Adaptive Forecast and Optimal Control Method for Grinding Wheel Infeed
p.87

Study of Intelligent Prediction Control of Surface Roughness in Grinding
p.93

Study on the Material Removal Mechanism of Precision Surface Grinding of Nanostructured WC/12Co Coating
p.99

Research on ELID Grinding of Nano-Cemented Carbide Tool
p.105

A New Grinding Method for Large-Scale Surface of Revolution and Error Analyzing of Its Wheel Dressing
p.111

Study on Infrared Ray Detecting Temperature and Network Technology for Grinding
p.117

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Study of Intelligent Prediction Control of Surface Roughness in Grinding

Abstract:

A surface roughness intelligent prediction control system during grinding is built. The system is composed of fuzzy neural network prediction subsystem and fuzzy neural network controller. In the fuzzy neural network prediction subsystem, the vibration data are added to the inputs besides the grinding condition, such as feed and speed, so as to improve the dynamic performance of the prediction subsystem. The fuzzy neural network controller is able to adapt grinding parameters in process to improve the surface roughness of machined parts when the roughness is not meeting requirements. Experiment verifies that the developed prediction control system is feasible and has high prediction and control accuracy.