论文标题
一种用于求解高级制造和工程应用中传热方程的机器学习方法
A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications
论文作者
论文摘要
在制造和工程应用中，在烤箱中加热零件的制造和工程应用中，开发了一个物理信息的神经网络以及对流传热PDE作为边界条件（BCS）的对流传热PDE作为边界条件（BCS）。由于对流系数通常是未知的，因此基于反复试验和错误有限元（FE）模拟的当前分析方法很慢。损失函数是根据满足PDE，BCS和初始条件的错误定义的。开发了自适应归一化方案，以同时减少损失条款。此外，传热理论用于特征工程。通过与FE结果进行比较来验证1D和2D病例的预测。结果表明，使用工程功能，可以预测训练区以外的热传递。受过训练的模型允许对一系列BC进行快速评估以开发反馈循环，从而实现了基于传感器数据的主动制造控制概念。
A physics-informed neural network is developed to solve conductive heat transfer partial differential equation (PDE), along with convective heat transfer PDEs as boundary conditions (BCs), in manufacturing and engineering applications where parts are heated in ovens. Since convective coefficients are typically unknown, current analysis approaches based on trial and error finite element (FE) simulations are slow. The loss function is defined based on errors to satisfy PDE, BCs and initial condition. An adaptive normalizing scheme is developed to reduce loss terms simultaneously. In addition, theory of heat transfer is used for feature engineering. The predictions for 1D and 2D cases are validated by comparing with FE results. It is shown that using engineered features, heat transfer beyond the training zone can be predicted. Trained model allows for fast evaluation of a range of BCs to develop feedback loops, realizing Industry 4.0 concept of active manufacturing control based on sensor data.
