基于神经网络模型的多尺度无otropic收益率函数
Hongchun Shang1,2, Lanjie Niu1,2, Zhongwang Tian1,2
1Science and Technology on Electromechanical Dynamic Control Laboratory, Xi'an 710065, China.
Materials (Basel, Switzerland)
|February 13, 2025
概括
一个全新的全连接神经网络 (FCNN) 模型简化了对合金的异型收益率函数校准. 这种方法通过准确预测材料异构性来增强有限元分析.
科学领域:
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
- 机器学习 机器学习
背景情况:
- 传统的异型收益率函数在参数校准和有限元素 (FE) 应用中存在挑战.
- 工程应用需要一个统一的模型来解决异性质材料行为的复杂性.
研究的目的:
- 开发和验证一个完全连接的神经网络 (FCNN) 模型,用于预测异型收益率表面.
- 将FCNN模型的性能与2090-T3合金的传统模型进行比较.
- 为FE分析创建一个统一的异型收益率函数子程序.
主要方法:
- 对2090-T3合金的四种传统异构型模型进行校准.
- 使用FCNN与应力元件 (α,β) 作为输入和异构性参数 (r) 作为输出.
- 通过晶体可塑性有限元 (CPFE) 模拟生成数据集,用于各种应力状态和负载方向.
主要成果:
- FCNN模型准确地预测了2090-T3合金的异构特性.
- FCNN的预测与传统模型校准结果保持一致.
- CPFE模拟证实了FCNN模型能够模拟异型行为.
结论:
- 基于FCNN的异型收益率函数提供了一个统一的方法,简化了子程序开发.
- 这种FCNN模型有效地捕捉了工程应用的复杂的异性质材料行为.
- 开发的FCNN子程序增强了有限元分析中异性态行为的建模.
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