一种人工神经网络的物理导向建模方法,用于在不规则的负载下预测多轴疲劳寿命
Tianguo Zhou1,2, Xingyue Sun1,2, Xu Chen1,2,3
1School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, People's Republic of China.
概括
一个新的物理引导的人工神经网络 (ANN) 模型增强了多轴疲劳寿命预测. 这种方法提高了不规则情况下的概括性,大大减少了预测错误.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 人工神经网络 (ANN) 难以对多轴疲劳寿命进行概括预测,特别是在不规则的负载场景中.
- 现有的模型缺乏有效地将先前的物理知识纳入疲劳分析的ANN架构的能力.
- 在复杂的条件下,提高ANN的推断能力对于可靠的疲劳寿命预测至关重要.
研究的目的:
- 开发人工神经网络 (ANN) 的物理导向建模方法,以增强多轴疲劳寿命预测.
- 将Basquin-Coffin-Manson方程中的物理知识引入到ANN结构中.
- 提高ANN对于不规则的多轴疲劳病例的概括和推断能力.
主要方法:
- 提出了一种以物理为导向的方法,灵感来自Basquin-Coffin-Manson方程.
- 该方法涉及在ANN模型的最后一个隐藏层中使用两个神经元.
- 对重量标志和偏差值的限制被应用于整合物理知识.
主要成果:
- 物理引导的ANN模型在预测多轴负载情况下的疲劳寿命方面表现出令人满意的表现.
- 该模型显示出更好的外推能力,在仅对常规病例进行训练后,准确预测不规则病例.
- 预测错误的显著减少:平均相对错误减少了33.29%,根平均平方误差 (RMSE) 减少了44.29%.
结论:
- 拟议的物理导向ANN方法有效地提高了多轴疲劳寿命预测的准确性和概括性.
- 将物理知识集成到ANN中,扩大了它们在结构完整性和疲劳分析中的应用范围.
- 这种方法为预测复杂,不规则的负载场景中的疲劳寿命提供了强大的解决方案,优于传统模型.
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