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Nickel based superalloys, such as Waspaloy, are extensively used to manufacture components that operate under high temperature cyclic loads, because of their superior chemical and thermo-mechanical properties. In particular, these artifacts are mainly produced by shaping and/or finishing machining operations. Despite of their huge properties, these alloys show an extremely poor workability, that makes them part of the group of the so called difficult-to-cut materials. Therefore, the proper selection of the machining conditions is always challenging for the designers. In this context, predictive models represent an extremely useful tool to numerically simulate the machining process, guaranteeing a good knowledge of the material behavior under machining conditions, and avoiding expansive and time consuming experimental campaigns. Besides, the proper selection of the material rheological model is of fundamental importance in order to obtain precise and affordable results from the numerical model. In this work a Johnson-Cook based viscoplastic flow behavior model was proposed. The model was obtained from Artificial Neural Network (ANN) based interpolation techniques and validated using orthogonal machining experimental tests. Moreover, the proposed model was compared with other rheological models available from literature to benchmark their affordability and assessing their performances.

Waspaloy orthogonal hard machining simulation, a comparison among different rheological models