Analytical Prediction of Cutting Tool Wear by Analysis of Wear Models

Abstract

The management of cutting tool wear and tool life is a long-standing topic in machining research and practice. Although a wide range of analytical and data-driven wear models has been proposed, their direct applicability in defining a robust tool life criterion and for practical tool life prediction remains limited. In this paper, we analyze several alternative functional forms for the flank wear–time (VB–t) relationship with the specific aim of defining an analytically tractable tool life criterion based on the minimum wear intensity and the inflection point of the wear curve. Linear, power, exponential and third-degree polynomial models are compared in terms of goodness of fit, monotonicity and the possibility of deriving closed-form expressions for the inflection point and the corresponding lifetime. Based on these criteria, monotonically increasing exponential and cubic polynomial models are identified as the most promising candidates. Their applicability is illustrated using a representative turning test, using a single measured wear curve as a case study rather than full statistical validation. The analysis shows that once the model parameters are identified for a given cutting system, the proposed framework can provide a transparent, analytically defined lifetime criterion and can support prediction of the remaining tool life. The work is therefore intended as a methodological contribution and as a starting point for future, more comprehensive experimental validation and for integration into digital tool condition monitoring systems.