一个多尺度建模和实验研究平面编织复合材料的拉伸强度与混合粘合螺栓连接
Jianwei Shi1, Junwei Zhang1, Kou Du2
1School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China.
Polymers
|July 27, 2024
概括
这项研究研究了碳纤维增强聚合物 (CFRP) 混合粘合螺栓 (HBB) 接头. 深度学习模型准确地预测了联合性能,为增强的复合结构提供了优化的设计.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 结构分析 结构分析
背景情况:
- 碳纤维增强聚合物 (CFRP) 混合粘合螺栓 (HBB) 接头是优于最佳的连接性能,集成粘合剂粘合和螺栓.
- 结构参数,如重叠的长度,螺栓孔间距,和合适的空隙显著影响CFRP HBB联合性能.
- 了解这些复杂的关系对于推进复合关节设计至关重要.
研究的目的:
- 通过使用多尺度有限元分析模型,研究结构参数对CFRP HBB接头强度的影响.
- 开发一种分析方法和反向设计方法来预测CFRP HBB接头的机械性能.
- 建立使用深度神经网络 (DNN) 的混合关节机械性能的快速计算方法.
主要方法:
- 开发用于CFRP HBB接头的多尺度有限元分析 (FEA) 模型.
- 对FEA模型进行实验验证,以评估单向拉伸失效力.
- 应用深度监督学习算法,特别是DNN,用于性能预测和反向设计.
主要成果:
- 该研究揭示了结构参数对CFRP HBB接头拉力失效力的复杂影响.
- 神经网络在预测各种参数组合的联合性能方面表现出高的准确性和效率.
- 成功提出了一种快速计算混合连接机械性能的方法.
结论:
- 这项研究阐明了CFRP HBB接头中机械性能和结构参数之间的复杂关系.
- 深度监督学习加速了复合关节结构的反向设计过程.
- 这些发现促进了复合材料及其连接方法的更广泛应用和更复杂的设计.
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