On-line tool wear monitoring in metal-cutting operations is essential for an on-line process optimization. In this paper, tool flank wear is estimated on-line by utilizing a nonlinear observer with the feedback of cutting force measurements. Based on a previously developed cutting process model for turning, a nonlinear observer is designed such that the estimated flank wear converges to the actual flank wear development in the presence of poor initial estimates. The stability analysis for the resulting observer error dynamic system is carried out using a physical limitation of the actual flank wear development and the Total Stability Theorem. The experimental results show that the proposed nonlinear observer estimates the flank wear quite well not only in the presence of poor initial estimates but also in the presence of unexpected fluctuations in the cutting force measurements. However, the method has drawbacks resulting from difficulties in obtaining accurate model parameters. An adaptive version of the presented nonlinear observer, periodically calibrated by off-line direct tool wear measurements using computer vision, is considered to be a promising strategy for industrial implementation.

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