NDLI: Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

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Journal of the Brazilian Society of Mechanical Sciences and Engineering : Volume 37, Issue 1, January 2015

Journal of the Brazilian Society of Mechanical Sciences and Engineering : Volume 36

Journal of the Brazilian Society of Mechanical Sciences and Engineering : Volume 35

Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

Content Provider	Springer Nature Link
Author	Bianchi, E. C. Marchi, Marcelo Moia, D. F. G. Thomazella, I. H. Aguiar, P. R. Martins, C. H. R.
Copyright Year	2014
Abstract	The grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg–Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as “sharp” (with cutting capacity) or “dull” (with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.
Starting Page	627
Ending Page	640
Page Count	14
File Format	PDF
ISSN	16785878
Journal	Journal of the Brazilian Society of Mechanical Sciences and Engineering
Volume Number	37
Issue Number	2
e-ISSN	18063691
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-05-21
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Dressing Acoustic emission Neural network Mechanical Engineering Grinding Tool condition monitoring
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Industrial and Manufacturing Engineering Automotive Engineering Mechanical Engineering Aerospace Engineering

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