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在带入硬化试验装置中使用物理信息的神经网络进行材料数据识别
Mohammad Zhian Asadzadeh1, Klaus Roppert2, Peter Raninger1
1Materials Center Leoben Forschung GmbH (MCL), Roseggerstraße 12, 8700 Leoben, Austria.
Materials (Basel, Switzerland)
|July 29, 2023
概括
基于物理学的神经网络 (PINNs) 准确地识别材料属性,使用来自感应硬化的温度数据. 这种方法对噪声有很强的抵抗力,有助于优化热处理.
科学领域:
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 基于物理学的神经网络 (PINNs) 将控制部分微分方程 (PDEs) 集成到损失函数中,用于解决复杂的问题.
- 在各种前向和反向问题中,PINNs已经取得了成功,证明了它们的多功能性.
研究的目的:
- 评估在感应硬化过程中使用PINNs用于材料属性识别的可行性.
- 使用温度传感器数据估计热物理性质,如比热和导热率.
主要方法:
- 使用的温度传感器数据和热方程与指定的边界条件.
- 采用PINNs来估计材料属性,通过有限元模型 (FEM) 基准数据进行验证.
- 研究了传感器放置和测量噪声对参数不确定性的影响.
主要成果:
- 即使虚拟温度传感器输入有限,PINNs也准确地识别了材料数据.
- 该方法表现出对测量噪声的稳定性,尽管收时间增加了.
- 分析了传感器位置和噪声水平对参数估计不确定性的影响.
结论:
- PINNs提供了一种准确而强大的方法,用于在感应硬化中离线材料数据估计.
- 这些发现对优化诱导热处理过程具有重大意义.
- 该研究验证了PINNs的使用与现实世界的测量数据用于材料特性.
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