开发用于热机械加工FeCrAl合金的构成关系,以预测热变形行为
Chuan Li1,2,3, Shuang Chen1, Shiyu Du2,3,4
1School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China.
Materials (Basel, Switzerland)
|July 12, 2025
概括
这项研究开发了一种人工神经网络 (ANN) 模型,以准确预测FeCrAl合金的热变形. 在模拟合金在热变形过程中的行为方面,ANN模型显著优于传统的阿雷尼乌斯模型.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算建模 计算建模
背景情况:
- 准确的构成模型对于FeCrAl合金覆盖管的数值模拟至关重要.
- 模拟的可靠性取决于热变形模型的预测准确度.
研究的目的:
- 开发和验证0Cr23Al5 FeCrAl合金的热变形的预测模型.
- 将人工神经网络 (ANN) 模型的预测精度与传统的阿雷尼乌斯模型进行比较.
主要方法:
- 对0Cr23Al5合金进行了热压缩试验,温度为850-1050°C,应变速率为0.1-10s-1.1.
- 基于实验数据,Arrhenius和人工神经网络 (ANN) 的构成模型被开发出来.
- 模型性能使用平均绝对相对误差 (AARE) 和根平均平方误差 (RMSE) 进行评估,并以ABAQUS.
主要成果:
- 该ANN模型实现了卓越的预测准确性,AARE为0.70%和RMSE为1.99 MPa.
- 阿雷尼乌斯模型的准确性较低,AARE为4.30%,RMSE为14.47MPa.
- ANN模型的预测与实验流应力数据具有很高的一致性,并且在ABAQUS模拟中具有可靠的性能.
结论:
- 开发的ANN模型有效地预测了热变形过程中FeCrAl合金的热学应力.
- 这项研究为FeCrAl合金的高保真构成模型提供了方法支持.
- 该ANN模型提供了一个可靠的构成模型,用于模拟FeCrAl合金的热力学行为.
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