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基于深度转移学习的线程连接变形的高效预测研究
Zhao Liu1, Zeyu Qi1, Hao Lu2,3
1State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing, 100871, China.
Scientific reports
|July 7, 2025
概括
本研究介绍了一种快速深度传输学习 (TL) 方法,用于预测在组合负载下线程连接变形. 它显著减少了计算时间,并提高了各种螺栓类型的准确性,提高了结构安全性.
科学领域:
- 机械工程 机械工程
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 螺纹连接在机械组件中至关重要,在各种负载下经历复杂的变形.
- 准确预测这些变形对于确保结构完整性和安全至关重要.
- 对于复杂的场景,诸如有限元分析 (FEA) 等现有方法可能是计算密集型的.
研究的目的:
- 开发一种高效的方法,以快速预测多维变形,在组合负载下的螺纹连接中.
- 利用深度转移学习 (TL) 来提高不同螺栓类型和负载条件的预测准确性和概括性.
- 为工程设计和结构评估提供传统FEA的计算成本有效的替代方案.
主要方法:
- 开发一种简化的静态分析模型,用于在联合负荷下进行变形预测,并与FEA进行验证.
- 在大型变形数据集上预训练深度神经网络 (DNN),以学习复杂的负载-变形关系.
- 应用转移学习 (TL) 来适应DNN用于各种螺栓类型和加载场景,使用拉丁式超立方样本 (LHS) 来生成数据集.
主要成果:
- 拟议的TL方法显著降低了计算成本,与复杂的负载条件相比,FEA只需要0.14%的计算时间.
- 在应用转移学习后,不同螺栓类型的预测准确率高达96.6%.
- 该方法准确模拟非线性变形,并显示出强大的概括能力.
结论:
- 开发的深度传输学习方法为预测在组合负载下线程连接变形提供了高效和准确的解决方案.
- 这种方法比FEA提供了实质性的计算优势,使工程设计和结构分析的实时应用成为可能.
- 该研究强调了人工智能驱动的方法在提高机械元件设计的安全性和效率方面的潜力.
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