使用人工神经网络来预测复合三明治结构的曲行为
Mortda Mohammed Sahib1,2, György Kovács1
1Faculty of Mechanical Engineering and Informatics, University of Miskolc, 3515 Miskolc, Hungary.
Polymers
|February 13, 2025
概括
人工神经网络 (ANN) 准确地模拟复合材料三明治结构,有效地预测偏移和应力. 这种低计算成本的方法有助于设计和优化工程应用的结构.
科学领域:
- 机械工程 机械工程
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
背景情况:
- 人工神经网络 (ANN) 对机械结构建模越来越感兴趣.
- 复合材料三明治结构提供轻量级和高性能特征.
- 需要有效的建模技术来预测结构行为.
研究的目的:
- 调查ANN用于模拟复合三明治结构的使用.
- 以较低的计算成本预测结构屈曲和面板应力.
- 根据有限元模型 (FEM) 和实验测试验证ANN预测.
主要方法:
- 利用ANN来预测结构屈曲和面板应力.
- 考虑各种设计变量的生成数据:面板材料,层数,核心类型,核心厚度和负载大小.
- 采用蒙特卡洛采样进行结构分析,并对织成的碳纤维增强聚合物 (WCFRP) 三明治结构与Nomex蜂核心进行实验测试.
主要成果:
- ANN的预测与FEM的结果非常相匹配.
- 实验验证证了ANN预测的准确性.
- 开发的ANN模型展示了一种低计算成本的方法.
结论:
- ANNs提供了一种有效和高效的方法来建模复合材料三明治结构.
- 这种技术为设计和优化三明治结构提供了有价值的工具.
- 该研究强调了ANN在需要结构分析的各种工程应用中的潜力.
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