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基于BP神经网络的TC6钛合金富氧α层厚度与热暴露温度、时间关系预报
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Relationship Prediction between the Thickness of Oxygen-enriched α Layer, Thermal Exposure Temperature and Holding Time for TC6 Titanium Alloy Based on BP Neural Network
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    摘要:

    将TC6钛合金经过不同热暴露工艺处理后,测得其富氧α层的厚度。利用BP人工神经网络建立富氧α层的厚度与热暴露工艺(热暴露温度、保温时间)之间的关系网络模型。结果表明:所建的模型可以很好的预报不同热暴露工艺下富氧α层厚度,而且还可以通过富氧α层厚度得到最优的热暴露工艺。建立了富氧α层厚度为50 μm热暴露工艺临界图以便指导生产实践。

    Abstract:

    A series of thickness of oxygen-enriched α layer were obtained after TC6 titanium alloy were treated with different thermal exposure processing (thermal exposure temperature, holding time). Their relationship network model was built by BP artificial neural network. The results show that the built model can be used for the prediction of the thickness of oxygen-enriched α layer of TC6 titanium alloy under different thermal exposure treatments. Meanwhile, the model can also serve as a guide for the heat treatment of TC6 titanium alloy if the thickness is given.

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罗连波,朱景川,刘 勇,陈志旋.基于BP神经网络的TC6钛合金富氧α层厚度与热暴露温度、时间关系预报[J].稀有金属材料与工程,2014,43(4):946~950.[Luo Lianbo, Zhu Jingchuan, Liu Yong, Chen Zhixuan. Relationship Prediction between the Thickness of Oxygen-enriched α Layer, Thermal Exposure Temperature and Holding Time for TC6 Titanium Alloy Based on BP Neural Network[J]. Rare Metal Materials and Engineering,2014,43(4):946~950.]
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  • 收稿日期:2013-04-11
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  • 在线发布日期: 2014-07-30
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