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构成模型和机器学习模型的应用,以预测TiAl合金的高温流动行为

Rui Zhao¹, Jianchao He¹, Hao Tian²

¹Institute of Special Environment Physical Sciences, Harbin Institute of Technology, Shenzhen 518055, China.

Materials (Basel, Switzerland)

|July 29, 2023

概括

这项研究研究了Ti46Al2Cr2Nb合金中的热变形. 结合人工神经网络 (ANN) 和塞拉斯 (SCS) 预测的混合模型为流量压力预测提供了卓越的概括.

科学领域:

材料科学材料科学材料科学
金工业是金工业的一个方面.
机械工程机械工程

背景情况:

了解- (TiAl) 合金的热变形行为对于优化制造工艺至关重要.
精确预测流应力对于模拟和控制高温成型过程中的塑料变形至关重要.
现有的构成型和数据驱动型号在预测不同条件下的流量压力方面存在局限性.

研究的目的:

为了研究Ti46Al2Cr2Nb合金的热变形特性.
评估和比较各种构成型和数据驱动型模型的流应力预测准确度和概括能力.
开发一个改进的预测模型,对TiAl合金进行增强的概括.

主要方法:

对Ti46Al2Cr2Nb合金进行了热压缩试验,温度范围为910-1060°C,应变速率为0.001至0.1s-1.
用三种构成模型 (包括SCS) 和三种数据驱动模型 (包括ANN) 来预测实验流应力数据.
模型的性能根据准确性 (确定系数,R2) 和在变化和新变形条件下的概括能力进行了评估.

主要成果:

对于TiAl合金的热变形的激活能量被确定为319kJ/mol.
人工神经网络 (ANN) 模型在已知的条件下对已插入和抽取的菌株表现出高精度 (R^2>0.98).
塞拉斯 (SCS) 模型在外推菌株的准确性较低 (R²<0.5),在新的变形条件下,ANN和SCS模型的表现不佳.
结合SCS和ANN预测的混合模型在不同的变形模式中表现出卓越的概括能力.

结论:

在已知的变形参数内,ANN模型在预测流应力方面表现出色,而SCS模型在外推方面效率较低.
单独的ANN和SCS模型都没有足够的概括性来预测在全新的加工条件下流量应力.
混合SCS-ANN模型提供了一个有前途的方法,可以在更广泛的条件下对TiAl合金进行强大和准确的流量应力预测.

关键词:

这是一种TiAl合金.数据驱动模型是基于数据的模型.概括能力概括能力的能力.热变形行为

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