A ROBUST APPROACH TO REAL-TIME TOOL WEAR ESTIMATION

This paper presents a robust strategy for estimating tool wear in meta l-cutting operations. The proposed estimation algorithm consists of tw o components: a recurrent neural network to model the tool wear dynami cs, and it robust observer to estimate the tool wear from this model u sing measurements of cutting force. The authors show that the algorith m ensures that the tool wear estimation error is uniformly bounded in the presence of bounded unmodelled effects, and that the ultimate boun d 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 s hown to provide wear estimates that are in close agreement with experi mental results.