While moving towards complete automation of the grinding process in order t
o be able to realise unattended manufacturing, it becomes mandatory to clos
ely monitor the process in order to detect any malfunction at the earliest
moment with high reliability. In the grinding process, a proper estimate of
the life of the grinding wheel is very useful. When this life expires, red
ressing is necessary. Generally, chatter marks, surface roughness, burn mar
ks, etc. are considered as the tool-life limit in grinding. In this paper,
the occurrence of burn marks on the work surface is adopted as a criterion
of the wheel life; accordingly, the time of the occurrence of grinding burn
during the cylindrical plunge grinding of steel is studied under different
conditions of grinding. The data thus collected is used for the prediction
of the time to burn using an artificial neural network. (C) 1999 Elsevier
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