A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications

By Navid Zobeiry, Keith D. Humfeld. Engineering applications of artificial intelligence, 2020

384 citations recorded by the source corpus.

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.

Subfields: Mechanical Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a1b56ac4334b3900a55d5fd0cb72beb27c03b852

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Mechanical Engineering

1. Ehsan Haghighat

In Statistical and Nonlinear Physics

1. Ehsan Haghighat
2. C. Ooi
3. Yew-Soon Ong
4. Trevor Campbell
5. Pao-Hsiung Chiu
6. S. Niaki
7. Xinglong Li
8. My Ha Dao

Citations in the corpus

• CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method (2021, background): “PINNs have been demonstrated for various physics phenomena, including heat transfer [22,23], fluid dynamics [24–26] and electromagnetic propagation [27–29].”
• Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture (2020, methodology): “In concurrent work, Zobeiry and Humfeld [46] developed a PINN to solve the heat transfer PDE in a composite-only system (no tool), without resin reaction kinetics, and for a cure cycle excluding the cool-down regime of the processing.”
• Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, neutral): “5) [215] [239] [286] [288] [66] [184] [214] [236] [304] [78] [97] [136] [275] [204] [183] [22] [85] [305] [54] [224] [50] [66] [180] [241] [25] [58] [141] [83] [285] [204] [183] [212] [137] [187] [160] [177]”