缺陷驱动的物理信息神经网络框架用于增材制造材料的疲劳寿命预测
Lanyi Wang1, Shun-Peng Zhu1, Changqi Luo1
1School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
概括
一个新的缺陷驱动的物理信息神经网络 (PiNN) 准确地预测了增材制造组件的疲劳寿命. 这种方法增强了对小样本条件的概括性,改善了结构完整性的评估.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 增材制造 (AM) 提供了设计自由,但存在缺陷,影响了部件疲劳性能和可靠性.
- 准确的疲劳寿命预测对于AM组件的实际应用至关重要.
- 现有的方法很难完全解释缺陷变化及其对疲劳散射的影响.
研究的目的:
- 开发一种新的缺陷驱动物理信息神经网络 (PiNN),用于评估缺陷对AM组件疲劳性能的影响.
- 提高机器学习模型的预测准确度和概括能力,以在有限的数据条件下预测疲劳寿命.
- 为在结构完整性应用中将物理知识集成到神经网络中提供一个强大而可扩展的框架.
主要方法:
- 开发了一个缺陷驱动的物理信息神经网络 (PiNN),将关键缺陷信息直接集成到模型的损失函数中.
- 该PiNN的训练和验证是使用各种增材制造材料的疲劳寿命预测数据进行的.
- 该模型的性能与基于传统骨折力学的PiNN方法进行了比较.
主要成果:
- 缺陷驱动的PiNN显示出显著改善的概括能力,特别是在小样本条件下.
- 拟议的PiNN实现了更高的准确性,并且在不依赖特定的断裂力学模型的情况下提供了物理一致的预测.
- 该框架被证明是可扩展的,允许整合额外的先前知识以提高性能.
结论:
- 与现有的方法相比,缺陷驱动的PiNN为AM组件的疲劳寿命预测提供了更好的方法.
- 这种基于物理的机器学习框架提高了增材制造部件的可靠性和适用性.
- 该研究提出了一种可扩展的方法,通过人工智能和材料科学集成来推进结构完整性评估.
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