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对经过修改的约翰逊-库克模型和人工神经网络进行比较分析,用于在BN增强的AZ80复合物中预测流应力
Ayoub Elajjani1,2, Shaochuan Feng1,2, Chaoyang Sun1,2
1School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, People's Republic of China.
化 (BN) 增强了合金AZ80. 一个人工神经网络 (ANN) 模型准确地预测了这种复合的神经网络.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算机建模 计算建模
背景情况:
- 化 (BN) 是一种具有出色性能的二维材料.
- 用BN增强AZ80合金可以提高机械性能.
研究的目的:
- 调查和预测在热变形过程中用BN增强的AZ80合金的机械性能增强.
- 为了比较修改后的约翰逊-库克 (MJC) 模型和人工神经网络 (ANN) 的预测准确度.
主要方法:
- 在各种温度和应变率下进行了热压缩试验.
- 一个修改后的约翰逊-库克 (MJC) 构成模型被开发出来.
- 使用实验数据训练了一个人工神经网络 (ANN).
主要成果:
- 该MJC模型实现了0.96的相关系数 (R) 和6.28%的平均绝对相对误差 (AARE).
- 该ANN模型将相关系数提高到R=0.99,并将AARE降低到1.5%以下.
- 该ANN在预测复杂的变形行为方面表现出卓越的准确性.
结论:
- 与MJC模型相比,ANN模型显著提高了BN增强的AZ80复合材料的预测准确性.
- 精确预测质行为有助于优化这些先进材料的工业加工.
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