In this study, the flow stress of Ti-6Al-4V during hot deformation was modeled using a decision tree algorithm. Hot compression experiments for Ti-6Al-4V in a Gleeble-3500 thermomechanical simulator were performed under a strain rate of 0.002–20 s–1
and temperatures of 575–725 °C. After the experiments, flow stress behavior was modeled, first by a traditional Arrhenius type equation, second by utilizing the artificial neural network, and lastly, with the aid of the decision tree algorithm. While the characteristics of measured flow stress were noticeably dependent on the resulting strain rate and temperature, the modeling accuracy regarding the flow stress results of the Arrhenius type equation, neural network approach and decision tree algorithm were compared. The decision tree algorithm predicted the flow stress most effectively.
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