用物理规律指导去噪，提升3D打印传感器数据质量
Physics-guided denoiser network for enhanced additive manufacturing data quality
- 结合物理模型与能量约束，实现噪声抑制与物理解释性统一
- 在多种噪声水平下优于传统神经网络去噪方法
- 适用于低成本传感器数据，适合工业实时监控场景
现代工程系统广泛部署传感器以实现实时监测与决策，但传感器采集的数据常含噪声且难以解读，限制了其在控制与诊断中的应用。本文提出一种物理信息引导的去噪框架，融合基于能量的模型与Fisher评分正则化，协同降低数据噪声并确保与物理模型的一致性。该方法首先在简谐振子、Burgers方程和拉普拉斯方程等基准问题上验证，覆盖多种噪声水平。随后应用于激光粉末床熔融（LPBF） additive manufacturing 实验的热辐射数据，利用训练好的物理信息神经网络（PINN）替代模型指导去噪。结果表明，所提方法在不同LPBF工艺条件下均优于基线神经网络去噪器，有效降低噪声。该物理引导去噪策略实现了低成本传感器数据的鲁棒实时解析，助力预测性控制与缺陷缓解。
原文摘要 · Abstract (English)
Modern engineering systems are increasingly equipped with sensors for real-time monitoring and decision-making. However, the data collected by these sensors is often noisy and difficult to interpret, limiting its utility for control and diagnostics. In this work, we propose a physics-informed denoising framework that integrates energy-based model and Fisher score regularization to jointly reduce data noise and enforce physical consistency with a physics-based model. The approach is first validated on benchmark problems, including the simple harmonic oscillator, Burgers' equation, and Laplace's equation, across varying noise levels. We then apply the denoising framework to real thermal emission data from laser powder bed fusion (LPBF) additive manufacturing experiments, using a trained Physics-Informed Neural Network (PINN) surrogate model of the LPBF process to guide denoising. Results show that the proposed method outperforms baseline neural network denoisers, effectively reducing noise under a range of LPBF processing conditions. This physics-guided denoising strategy enables robust, real-time interpretation of low-cost sensor data, facilitating predictive control and improved defect mitigation in additive manufacturing.
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