Physics-guided denoiser network for enhanced additive manufacturing data quality

• URL: http://arxiv.org/abs/2508.02712v1
• Date: Thu, 31 Jul 2025 05:21:02 GMT
• Title: Physics-guided denoiser network for enhanced additive manufacturing data quality
• Authors: Pallock Halder, Satyajit Mojumder,
• Abstract summary: We propose a physics-informed denoising framework that integrates energy-based model and Fisher score regularization.<n>We then apply the framework to real thermal emission data from laser powder bed fusion (LPBF) additive manufacturing experiments.<n>Results show that the proposed method outperforms baseline neural network denoisers, effectively reducing noise under a range of LPBF processing conditions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Unsupervised CP-UNet Framework for Denoising DAS Data with Decay Noise [13.466125373185399]
  Distributed acoustic sensor (DAS) technology leverages optical fiber cables to detect acoustic signals.<n>DAS exhibits a lower signal-to-noise ratio (S/N) compared to geophones.<n>This reduced S/N can negatively impact data analyses containing inversion and interpretation.
  arXiv  Detail & Related papers  (2025-02-19T03:09:49Z)
• Impact of Noisy Supervision in Foundation Model Learning [91.56591923244943]
  This paper is the first work to comprehensively understand and analyze the nature of noise in pre-training datasets.<n>We propose a tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise and improve generalization.
  arXiv  Detail & Related papers  (2024-03-11T16:22:41Z)
• Physics-Informed Data Denoising for Real-Life Sensing Systems [30.80700186287102]
  We develop a physics-informed denoising model for noisy sensors. Our method can denoise data in real time (4ms for a sequence of 1s) for low-cost noisy sensors.
  arXiv  Detail & Related papers  (2023-11-12T21:25:56Z)
• Physics-guided Noise Neural Proxy for Practical Low-light Raw Image Denoising [22.11250276261829]
  Recently, the mainstream practice for training low-light raw image denoising has shifted towards employing synthetic data. Noise modeling, which focuses on characterizing the noise distribution of real-world sensors, profoundly influences the effectiveness and practicality of synthetic data. We propose a novel strategy: learning the noise model from dark frames instead of paired real data, to break down the data dependency.
  arXiv  Detail & Related papers  (2023-10-13T14:14:43Z)
• Learning Provably Robust Estimators for Inverse Problems via Jittering [51.467236126126366]
  We investigate whether jittering, a simple regularization technique, is effective for learning worst-case robust estimators for inverse problems. We show that jittering significantly enhances the worst-case robustness, but can be suboptimal for inverse problems beyond denoising.
  arXiv  Detail & Related papers  (2023-07-24T14:19:36Z)
• Realistic Noise Synthesis with Diffusion Models [44.404059914652194]
  Deep denoising models require extensive real-world training data, which is challenging to acquire.<n>We propose a novel Realistic Noise Synthesis Diffusor (RNSD) method using diffusion models to address these challenges.
  arXiv  Detail & Related papers  (2023-05-23T12:56:01Z)
• On the uncertainty analysis of the data-enabled physics-informed neural network for solving neutron diffusion eigenvalue problem [4.0275959184316825]
  We investigate the performance of DEPINN in calculating the neutron diffusion eigenvalue problem from several perspectives. In order to reduce the effect of noise and improve the utilization of the noisy prior data, we propose innovative interval loss functions. This paper confirms the feasibility of the improved DEPINN for practical engineering applications in nuclear reactor physics.
  arXiv  Detail & Related papers  (2023-03-15T08:59:03Z)
• Digital noise spectroscopy with a quantum sensor [57.53000001488777]
  We introduce and experimentally demonstrate a quantum sensing protocol to sample and reconstruct the auto-correlation of a noise process. Walsh noise spectroscopy method exploits simple sequences of spin-flip pulses to generate a complete basis of digital filters. We experimentally reconstruct the auto-correlation function of the effective magnetic field produced by the nuclear-spin bath on the electronic spin of a single nitrogen-vacancy center in diamond.
  arXiv  Detail & Related papers  (2022-12-19T02:19:35Z)
• Pre-training via Denoising for Molecular Property Prediction [53.409242538744444]
  We describe a pre-training technique that utilizes large datasets of 3D molecular structures at equilibrium. Inspired by recent advances in noise regularization, our pre-training objective is based on denoising.
  arXiv  Detail & Related papers  (2022-05-31T22:28:34Z)
• Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders [62.997667081978825]
  We propose a Fully Convolutional Denoising Autoencoder, which learns to produce a clean neuronal activity signal from a noisy multichannel input. The experimental results on simulated data show that our proposed method can improve significantly the quality of noise-corrupted neural signals.
  arXiv  Detail & Related papers  (2021-09-18T14:51:24Z)
• Learning Model-Blind Temporal Denoisers without Ground Truths [46.778450578529814]
  Denoisers trained with synthetic data often fail to cope with the diversity of unknown noises. Previous image-based method leads to noise overfitting if directly applied to video denoisers. We propose a general framework for video denoising networks that successfully addresses these challenges.
  arXiv  Detail & Related papers  (2020-07-07T07:19:48Z)
• Robust Processing-In-Memory Neural Networks via Noise-Aware Normalization [26.270754571140735]
  PIM accelerators often suffer from intrinsic noise in the physical components. We propose a noise-agnostic method to achieve robust neural network performance against any noise setting.
  arXiv  Detail & Related papers  (2020-07-07T06:51:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.