Abstract
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.
Highlights
Ti-6Al-4V is a titanium alloy consisting of Al (6 wt%) and v (4 wt%)
The calculated stresses are marked in the figure with the experimental curves at a strain rate of (a) 0.002 s−1, (b) 0.02 s−1, (c) 2 s−1 and (d) 20 s−1 and temperature range of 575–725 °C
Hot deformation experiments for Ti-6Al-4V were performed and the flow stresses were modeled by an Arrhenius equation, neural network, and a decision tree algorithm
Summary
The alloy is known for its good mechanical properties, toughness, high strength, low density and corrosion resistance [1,2,3]. Since it has superior properties, the alloy is used in a variety of areas such as pressure vessels, automobile parts, aerospace, as well as used for the manufacturing of medical parts [2,4,5,6]. The alloy is used to manufacture intake valves in the automobile industry [5]. The alloy is used for surgical implants, due to its ability to perform satisfactorily in the temperature range of 23 ◦ C to 150 ◦ C [6]
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