Real-time tool wear estimation using cutting force measurements

Presents a robust strategy for estimating tool wear in metal cutting operations. The proposed estimation algorithm consists of two components: a recurrent neural network to model the tool wear dynamics, and a robust observer to estimate the tool wear from this model using measurements of cutting force. It is shown that the algorithm ensures that the tool wear estimation error is uniformly bounded in the presence of bounded unmodeled effects, and that the ultimate bound on this error can be made as small as desired. The proposed approach is applied to the problem of estimating tool wear in turning and is shown to provide wear estimates which are in close agreement with published experimental results.

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