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Tool life is an important parameter in machining processes, affecting directly the quality of machined components and the process cost. It is already shown that various parameters can affect tool life such as process parameters, i.e. depth of cut, cutting speed and feed, or material properties of cutting tool and workpiece. The determination of the effect of each parameter on tool life is of crucial importance when designing the manufacturing process of a product in order to select suitable process parameter values and tool types. Several empirical formulas for the determination of tool life exist in the relevant literature; especially in the case of CBN cutting tools for turning, a cubic polynomial formula was proposed to model the relationship between tool life and cutting speed. The determination of the polynomial parameters was performed by conducting cutting experiments for several cutting speeds, without the aid of a design of experiments (DoE) method in order to model properly this non-linear relationship. In this paper, the feasibility of determining this non-linear relationship by conducting experiments designed by Taguchi method and using artificial neural networks (ANN) is investigated for several cases and conclusions on the applicability of this approach are presented

Applicability of ANN models and Taguchi method for the determination of tool life in turning