Physics-guided denoiser network for enhanced additive manufacturing data quality
This work addresses data quality issues for real-time monitoring in additive manufacturing, enabling better control and defect mitigation, though it appears incremental as it builds on existing physics-informed neural network approaches.
The paper tackles the problem of noisy sensor data in additive manufacturing by proposing a physics-informed denoising framework that integrates energy-based models and Fisher score regularization, which outperforms baseline neural network denoisers on real thermal emission data from laser powder bed fusion experiments.
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.