A deep belief network to predict the hot deformation behavior of a Ni-based superalloy
The hot deformation behavior of a Ni-based superalloy is studied by hot compressive experiments. The true stress is found to be highly affected by the deformation parameters, including strain rate and deformation temperature. The true stress dramatically decreases with decreasing strain rate or incr...
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Veröffentlicht in: | Neural computing & applications 2018-06, Vol.29 (11), p.1015-1023 |
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Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | The hot deformation behavior of a Ni-based superalloy is studied by hot compressive experiments. The true stress is found to be highly affected by the deformation parameters, including strain rate and deformation temperature. The true stress dramatically decreases with decreasing strain rate or increasing deformation temperature. A deep belief network (DBN) model is developed for predicting true stress of the studied superalloy based on the experimental data. The structure of the developed DBN model is optimized layer by layer. The high accuracy indicates that the developed DBN model is able to effectively characterize the hot deformation behavior of the studied Ni-based superalloy. Moreover, the developed DBN model also has an excellent interpolation ability. |
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ISSN: | 0941-0643 1433-3058 |
DOI: | 10.1007/s00521-016-2635-7 |