在热压缩条件下对 (2.5vol%TiB + 2.5vol%TiC) /TC4复合材料的构成建模研究
Kehao Qiang1, Shisong Wang2, Haowen Wang3
1College of Materials and Chemistry and Chemical Engineering, Chengdu University of Technology, Chengdu 610059, China.
Materials (Basel, Switzerland)
|April 9, 2024
概括
了解矩阵复合材料的热变形是部件性能的关键. 这项研究使用阿雷尼乌斯和BP神经网络开发了准确的构成模型,发现BP神经网络在预测流量压力方面优越.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 金工业是金工业的一个方面.
背景情况:
- 矩阵复合材料的热变形行为对组件性能至关重要.
- 建立准确的构成模型,将变形参数和流应力联系起来是非常重要的.
研究的目的:
- 为了研究 (2.5电%TiB + 2.5电%TiC) /TC4.4的热变形行为.
- 使用阿雷尼乌斯和BP神经网络算法建立和比较构成模型.
- 为了评估这些模型对热压缩流应力的预测准确性.
主要方法:
- 在 (2.5电量%TiB + 2.5电量%TiC) /TC4.4上进行的热压缩实验.
- 温度在1013.15到1133.15K之间;应变速率在0.001到0.1s-1之间.
- 使用基于压力-应变数据的Arrhenius和BP神经网络算法开发的构成模型.
主要成果:
- 流应力随着温度的下降和延展率的增加而增加.
- 应力-应变曲线表现出工作硬化和软化.
- 两种模型都有效地预测了流应力,BP神经网络显示出更高的准确性 (更小的错误,更高的相关性).
结论:
- 该BP神经网络算法提供了一个更准确,更可靠的预测热压缩流应力 (2.5vol%TiB + 2.5vol%TiC) / TC4.4.
- 精确的构成模型对于优化这些复合材料的热工作过程至关重要.
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