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SURFACE ROUGHNESS IN GRINDING: ON-LINE PREDICTION WITH ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

By Murad S. Samhouri and Brian W. Surgenor

Abstract

Grinding, roughness prediction, adaptive neurofuzzy An on-line monitoring and prediction of surface roughness in grinding is introduced with experimental verification. An adaptive neurofuzzy inference system (ANFIS) is used to monitor and identify the surface roughness online. The system uses a piezoelectric accelerometer to generate a signal related to grinding features and surface finish. The power spectral density (PSD) of this signal is used as an input to ANFIS, which in turn outputs a value for the on-line predicted surface roughness. Different neuro-fuzzy parameters were adopted during the training process of ANFIS in order to improve the on-line monitoring and prediction accuracy of surface roughness. Experimental validation runs were conducted to compare the measured surface roughness values with the online-predicted ones. The comparison shows that the adoption of Bell-shaped membership function in ANFIS achieved a very satisfactory on-line prediction accuracy of 91%

Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.8967
Provided by: CiteSeerX
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