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Title: Grinding control using artificial neural networks with AE feedback

Authors: Kexin Wang; Xianjun Sheng

Addresses: School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China ' Department of Electrical and Electronics Engineering, Dalian University of Technology, Dalian 116024, China

Abstract: The surface roughness of workpiece in the process of ceramic grinding is affected by many factors. An online monitoring system for grinding roughness is proposed in this paper. This system monitors the AE signals that emerged in grinding to automatically identify wheel state and its parameters, wheel and workpiece speed, which inturn is used to control the workpiece roughness. We demonstrate the proposed system for internal grinding of ceramic work-piece with complex generatrix, and the results of experiments shows that the roughness is efficiently controlled.

Keywords: ceramic grinding; surface roughness; surface quality; online monitoring; acoustic emission; AE feedback; grinding control; artificial neural networks; ANNs; ceramics; grinding wheel parameters; grinding wheel speed; workpiece speed.

DOI: 10.1504/IJMISSP.2013.052870

International Journal of Machine Intelligence and Sensory Signal Processing, 2013 Vol.1 No.1, pp.55 - 67

Received: 02 Apr 2012
Accepted: 22 May 2012

Published online: 19 Mar 2013 *

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