Automatic detection of thermal damage in grinding process by artificial neural network

This work aims to develop an intelligent system for detecting the workpiece burn in the surface grinding process by utilizing a multi-perceptron neural network trained to generalize the process and, in turn, obtnaing the burning threshold. In general, the burning occurrence in grinding process can be detected by the DPO and FKS parameters. However, these ones were not efficient at the grinding conditions used in this work. Acoustic emission and electric power of the grinding wheel drive motor are the input variable and the output variable is the burning occurrence to the neural network. In the experimental work was employed one type of steel (ABNT-1045 annealed) and one type of grinding wheel referred to as TARGA model ART 3TG80.3 NVHB.

Bibliographic Details

Main Authors:	Dotto,Fábio Romano Lofrano, Aguiar,Paulo Roberto de, Bianchi,Eduardo Carlos, Flauzino,Rogério Andrade, Castelhano,Gustavo de Oliveira, Pansanato,Landry
Format:	Digital revista
Language:	English
Published:	Escola de Minas 2003
Online Access:	http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0370-44672003000400013
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