基于物理信息神经网络的高速视频熔池流场重建

中文摘要 AI 辅助

熔池流动显著影响丝材-电弧定向能量沉积（DED）中的焊道几何形状、缺陷形成和凝固微观组织。然而，由于极端温度、强烈电弧照明和复杂流动模式，直接流动传感仍然是一个开放挑战。传统研究要么依赖计算密集的流体动力学模拟，这些模拟需要假设边界条件且缺乏经验锚定，要么依赖基于视觉的示踪粒子跟踪，这会产生稀疏、噪声大且物理上不受约束的速度估计。本研究通过重建基于GTAW的丝材-电弧DED过程中不同流动状态（准稳态对流和周期性液滴冲击）下的物理约束二维表面流场，弥合了这两种范式。该流程首先进行单应性校正以校准倾斜相机视角，随后提取密集光流以提供原始速度观测。对于准稳态熔池，物理信息神经网络（PINN）通过复合损失最小化联合强制执行数据保真度、不可压缩纳维-斯托克斯动量方程、无穿透边界条件和电弧强迫先验，从而重建表面速度场。对于受周期性液滴冲击影响的熔池，进一步开发了相位条件时间相关PINN，以解析冲击周期内的循环流动演化。这项工作提供了一个统一框架，弥合了高速视觉传感与流体力学之间的鸿沟，为丝材-电弧DED过程实现基于物理的流动诊断。

英文摘要

Molten pool flow significantly influences bead geometry, defect formation, and solidification microstructure in wire-arc directed energy deposition (DED). However, direct flow sensing remains an open challenge due to extreme temperatures, intense arc illumination, and complex flow patterns. Traditional research has relied either on computationally intensive fluid dynamics simulations, which require assumed boundary conditions and lack empirical anchoring, or on vision-based tracer particle tracking, which yields sparse, noisy, and physically unconstrained velocity estimates. This work bridges these two paradigms by reconstructing physics-constrained 2D surface flow fields from high-speed imaging of a GTAW-based wire-arc DED process under distinct flow regimes: quasi-steady convection and periodic droplet impact. The pipeline begins with homography correction to calibrate the oblique camera view, followed by dense optical flow extraction to provide raw velocity observations. For quasi-steady pools, a physics-informed neural network (PINN) reconstructs the surface velocity field by jointly enforcing data fidelity, incompressible Navier-Stokes momentum equations, no-penetration boundary conditions, and an arc-forcing prior through composite loss minimization. For pools subject to periodic droplet impact, a phase-conditioned time-dependent PINN is further developed to resolve the cyclic flow evolution throughout the impact period. This work provides a unified framework that bridges high-speed visual sensing and fluid mechanics, enabling physics-grounded flow diagnostics for wire-arc DED processes.

发表机构

• University of Kentucky（肯塔基大学）
• Hanoi University of Industry（河内工业大学）

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