软传感器建模用于基于多损失一致性优化PINNN的大型合金工件的3D过渡温度场
Ling Shen1, Zhipeng Chen2, Xinyi Wang2
1College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一种新的物理信息神经网络 (PINN),用于在热处理过程中在合金中精确的3D温度场建模. 采用MCO-PINN方法,可以使用稀疏的数据进行准确和高效的预测.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 在火过程中,均的温度分布对合金性能至关重要.
- 准确的3D短暂温度场预测对于大型合金工件的有效热处理至关重要.
研究的目的:
- 为大型合金工件建立3D过渡温度场模型.
- 为此模型的软传感提出基于多损失一致性优化的物理信息神经网络 (MCO-PINN).
主要方法:
- 使用具有高斯激活函数的多层感知器 (MLP) 开发了一个3D过渡温度场模型.
- 集成部分微分方程 (PDE) 余量,初始/边界条件,以及观察到的数据转化为网络损失函数.
- 提出了一种基于高斯概率分布和最大概率估计的权重一致性优化方法.
- 引入了自适应的初始值-自身矢量编码集群 (AIV-ECC) 算法,以提高训练速度和概括性.
主要成果:
- MCO-PINN准确地解决了3D过渡温度场模型.
- 该方法实现了高精度和时间效率.
- 通过模拟和使用稀疏测量的工业实验证明了有效性.
结论:
- MCO-PINN为合金中的3D短暂温度场建模提供了一个高度精确和时间效率高的解决方案.
- 拟议的方法能够基于有限的数据,对热处理过程进行有效的软传感.
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