Abstract
In this research, a mathematical description of hot flow curves of CuFe2 copper alloy has been assembled. Experimental flow curves of the investigated alloy were created on the basis of hot compression dataset. This dataset was acquired in the temperature range of 923-1223 K and the strain rate range of 0.1-10 s−1, with the maximum true strain value of 1.0. The experimental flow curves were described by two artificial neural network approaches. In the first case, a neural network has been created to approximate the experimental flow curves with respect to the true strain, strain rate and temperature. In the second case, a hybrid approach based on the combination of predictive models with neural networks has been utilized. In this case, five neural networks were used to describe parameters of these models in relation to the temperature and strain rate. Results have shown that the hybrid approach allows an accurate description of the experimental data and also provides more reliable prediction.
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The article was created thanks to the project No. CZ.02.1.01/0.0/0.0/17_049/0008399 from the EU and CR financial funds provided by the Operational Programme “Research, Development and Education” (Call 02_17_049 “Long-Term Intersectoral Cooperation for ITI”; Managing Authority: Czech Republic—Ministry of Education, Youth and Sports).
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Opěla, P., Schindler, I., Kawulok, P. et al. Hot Flow Curve Description of CuFe2 Alloy via Different Artificial Neural Network Approaches. J. of Materi Eng and Perform 28, 4863–4870 (2019). https://doi.org/10.1007/s11665-019-04199-5
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DOI: https://doi.org/10.1007/s11665-019-04199-5