Abstract
This paper deled with the Hopkinson bar experimental data by BP Neural Networks, including of the influence of the temperature and strain rate to the dynamic yield strength of the material. In general, the strain rate effect and the temperature effect on the material dynamic properties in the tests are coupled with each other. Through handling of the experimental data by the BP Network system, these two effects can be uncoupled and in this paper the curves of the material dynamic yield strength vs. strain rate with different temperatures and the curves of the material dynamic yield strength vs. temperature with different strain rates are presented.
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Liu, Xl., Song, Sc., Lei, T. (2011). Application of BP Neural Networks in Prediction of the Material Dynamic Properties. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21105-8_9
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DOI: https://doi.org/10.1007/978-3-642-21105-8_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21104-1
Online ISBN: 978-3-642-21105-8
eBook Packages: Computer ScienceComputer Science (R0)