Tool wear monitoring in milling operations: Preliminary results
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abstract
In our research we are developing an indirect method for on-line continuous estimation of tool flank wear in end milling operations. Indirect methods measure process variables that are correlated with tool wear, and, from these measurements an estimate of wear is made. Tool wear estimations are obtained by simultaneously using both data from sensors and knowledge of the severity of the cutting conditions. Data fusion is accomplished by a neural network based model. We present in this paper some initial results from our work, namely the correlations found among some AE parameters and the levels of flank wear, nose wear, and depth of cut notch. Additional results will be presented at the conference as they become available.
