用物理约束神经网络，快速预测激光打印中的热应力变化。
Thermal-Mechanical Physics Informed Deep Learning For Fast Prediction of Thermal Stress Evolution in Laser Metal Deposition
- 将热力学物理规律嵌入神经网络，指导模型学习。
- 仅需少量仿真数据，预测精度仍高于传统方法。
- 预训练模型可迁移使用，适合实时在线监测。
理解金属增材制造中热应力演化对提升构件质量至关重要。尽管机器学习在建模复杂多物理场问题方面展现出潜力，但基于物理的仿真计算成本高，而传统数据驱动模型又依赖大量标注数据。在金属增材制造中，通过实验或高保真仿真生成大规模训练数据极为昂贵。为此，本研究提出一种融合控制物理定律的物理信息神经网络（PINN）框架，用于预测激光金属沉积（LMD）过程中温度与热应力的演化。研究还表明，结合少量仿真数据后，PINN模型在准确性和效率上均有显著提升。此外，该模型具备良好迁移能力，可利用预训练的PINN模型作为在线软传感器，快速预测新工艺参数下的热应力分布，相比传统数值模型大幅降低计算时间，同时保持较高精度。
原文摘要 · Abstract (English)
Understanding thermal stress evolution in metal additive manufacturing (AM) is crucial for producing high-quality components. Recent advancements in machine learning (ML) have shown great potential for modeling complex multiphysics problems in metal AM. While physics-based simulations face the challenge of high computational costs, conventional data-driven ML models require large, labeled training datasets to achieve accurate predictions. Unfortunately, generating large datasets for ML model training through time-consuming experiments or high-fidelity simulations is highly expensive in metal AM. To address these challenges, this study introduces a physics-informed neural network (PINN) framework that incorporates governing physical laws into deep neural networks (NNs) to predict temperature and thermal stress evolution during the laser metal deposition (LMD) process. The study also discusses the enhanced accuracy and efficiency of the PINN model when supplemented with small simulation data. Furthermore, it highlights the PINN transferability, enabling fast predictions with a set of new process parameters using a pre-trained PINN model as an online soft sensor, significantly reducing computation time compared to physics-based numerical models while maintaining accuracy.
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