Modelling beta transus temperature of titanium alloys using artificial neural network |
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| Affiliation: | 1. Department of Mechanical Engineering, The University of Melbourne, Victoria 3010, Australia;2. Centre for Additive Manufacturing, School of Engineering, RMIT University, Melbourne, VIC 3000, Australia;3. Physics of Fluids Group, Max Planck Center for Complex Fluid Dynamics, J. M. Burgers Center for Fluid Dynamics and MESA+ Research Institute, Department of Science and Technology, University of Twente, 7500AE Enschede, the Netherlands |
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| Abstract: | An artificial neural network (ANN) model is developed to simulate the non-linear relationship between the beta transus (βtr) temperature of titanium alloys and the alloy chemistry. The input parameters to the model consist of the concentration of nine elements, i.e. Al, Cr, Fe, Mo, Sn, Si, V, Zr and O, whereas the model output is the βtr temperature. Good performance of the ANN model was achieved. The interactions between the alloying elements were estimated based on the obtained ANN model. The results showed good agreement with experimental data. The influence of the database scale on ANN model performance was also discussed. Estimation of βtr temperature through thermodynamic calculation was carried out as a comparison. |
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