Classification of burn degrees in grinding by neural nets

Abstract

One of the problems found in the implementation of intelligent grinding process is the automatic detection of surface burn of the parts. Several systems of monitoring have been assessed by researchers in order to control the grinding process and guarantee the quality of the ground parts. However, monitoring techniques still fails in certain situations where the phenomenon changes are not completely obtained by the employed signals. The aim of this work is to attain the classification of burn degrees of the parts ground with the utilization of neural networks. The acoustic emission and power signals as well as the statistics derived from the digital signal processing of these signals are utilized as inputs of the neural networks. A surface grinding machine with an aluminum oxide grinding wheel was used to grind parts of ANSI 1020 steels in the experimental tests. The results have shown the success of classification for most of the structures studied with the best result presented by the structure having the parameter referred to as DPO and depth of cut as inputs.