Real-time distortion prediction in metallic additive manufacturing via a physics-informed neural operator approach

• URL: http://arxiv.org/abs/2511.13178v1
• Date: Mon, 17 Nov 2025 09:37:04 GMT
• Title: Real-time distortion prediction in metallic additive manufacturing via a physics-informed neural operator approach
• Authors: Mingxuan Tian, Haochen Mu, Donghong Ding, Mengjiao Li, Yuhan Ding, Jianping Zhao,
• Abstract summary: This paper proposes a physics-informed smart Neuralhorizon Operator (PINO) to predict z and y-direction for the future 15 s.<n>The performance of PINO model highlights its potential for real-time long-time distortion field prediction in controlling defects.
• Score: 3.607834195988809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of digital twins and smart manufacturing systems, there is an urgent need for real-time distortion field prediction to control defects in metal Additive Manufacturing (AM). However, numerical simulation methods suffer from high computational cost, long run-times that prevent real-time use, while conventional Machine learning (ML) models struggle to extract spatiotemporal features for long-horizon prediction and fail to decouple thermo-mechanical fields. This paper proposes a Physics-informed Neural Operator (PINO) to predict z and y-direction distortion for the future 15 s. Our method, Physics-informed Deep Operator Network-Recurrent Neural Network (PIDeepONet-RNN) employs trunk and branch network to process temperature history and encode distortion fields, respectively, enabling decoupling of thermo-mechanical responses. By incorporating the heat conduction equation as a soft constraint, the model ensures physical consistency and suppresses unphysical artifacts, thereby establishing a more physically consistent mapping between the thermal history and distortion. This is important because such a basis function, grounded in physical laws, provides a robust and interpretable foundation for predictions. The proposed models are trained and tested using datasets generated from experimentally validated Finite Element Method (FEM). Evaluation shows that the model achieves high accuracy, low error accumulation, time efficiency. The max absolute errors in the z and y-directions are as low as 0.9733 mm and 0.2049 mm, respectively. The error distribution shows high errors in the molten pool but low gradient norms in the deposited and key areas. The performance of PINO surrogate model highlights its potential for real-time long-horizon physics field prediction in controlling defects.

Related papers

• Physics-informed Neural Operator Learning for Nonlinear Grad-Shafranov Equation [18.564353542797946]
  In magnetic confinement nuclear fusion, rapid and accurate solution of the Grad-Shafranov equation (GSE) is essential for real-time plasma control and analysis.<n>Traditional numerical solvers achieve high precision but are computationally prohibitive, while data-driven surrogates infer quickly but fail to enforce physical laws and generalize poorly beyond training distributions.<n>We present a Physics-Informed Neural Operator (PINO) that directly learns the GSE solution operator, mapping shape parameters of last closed flux surface to equilibrium solutions for realistic nonlinear current profiles.
  arXiv  Detail & Related papers  (2025-11-24T13:46:38Z)
• Physically consistent and uncertainty-aware learning of spatiotemporal dynamics [44.27000400517127]
  We introduce a physics-consistent neural operator (PCNO) that enforces physical constraints.<n>A physics-consistent projection layer within PCNO efficiently computes mass momentum conservation in Fourier space.<n>We also propose DiffPCNO, which leverages a diffusion consistency model to quantify and quantify uncertainties.
  arXiv  Detail & Related papers  (2025-10-23T22:17:21Z)
• Data-Driven Temperature Modelling of Machine Tools by Neural Networks: A Benchmark [2.07811670193148]
  We introduce a novel paradigm in which NNs are trained to predict high-fidelity temperature and heat flux fields within the machine tool.<n>The proposed framework enables subsequent computation and correction of a wide range of error types using modular, swappable downstream components.
  arXiv  Detail & Related papers  (2025-09-26T15:43:08Z)
• Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Laser Powder Bed Fusion [0.0]
  This study presents an efficient modeling framework termed FEA-Regulated Physics-Informed Neural Network (FEA-PINN)<n>The PINN model incorporates temperature-dependent material properties and phase change behavior using the apparent heat capacity method.<n>A comparative analysis shows that FEA-PINN achieves equivalent accuracy to FEA while significantly reducing computational cost.
  arXiv  Detail & Related papers  (2025-06-25T15:25:01Z)
• Modeling Membrane Degradation in PEM Electrolyzers with Physics-Informed Neural Networks [45.32169712547367]
  Proton exchange membrane (PEM) electrolyzers are pivotal for sustainable hydrogen production.<n>Their long-term performance is hindered by membrane degradation, which poses reliability and safety challenges.<n>Traditional physics-based models have been developed, offering interpretability but requiring numerous parameters that are often difficult to measure and calibrate.<n>This study presents the first application of Physics-Informed Neural Networks (PINNs) to model membrane degradation in PEM electrolyzers.
  arXiv  Detail & Related papers  (2025-06-19T15:46:49Z)
• Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis [5.055539099879598]
  This study investigates why physics-informed machine learning (PIML) can fail in macroscopic traffic flow modeling.<n>We define failure as cases where a PIML model underperforms both purely data-driven and purely physics-based baselines by a given threshold.<n>This explains why LWR-based PIML can outperform ARZ-based PIML even with high-resolution data, with the gap shrinking as resolution increases.
  arXiv  Detail & Related papers  (2025-05-16T17:55:06Z)
• Spatiotemporal Field Generation Based on Hybrid Mamba-Transformer with Physics-informed Fine-tuning [46.67399627400437]
  This research confronts the challenge of substantial physical equation discrepancies in the generation of physical fields through trained models.<n>A physical field generation model, named HMT-PF, is developed based on the hybrid Mamba-Transformer architecture.
  arXiv  Detail & Related papers  (2025-05-16T14:40:56Z)
• Thermal-Mechanical Physics Informed Deep Learning For Fast Prediction of Thermal Stress Evolution in Laser Metal Deposition [0.0]
  Understanding thermal stress evolution in metal additive manufacturing (AM) is crucial for producing high-quality components.<n>Recent advancements in machine learning (ML) have shown great potential for modeling complex multiphysics problems in metal AM.<n>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.
  arXiv  Detail & Related papers  (2024-12-25T05:37:48Z)
• Deep Neural Operator Enabled Digital Twin Modeling for Additive Manufacturing [9.639126204112937]
  A digital twin (DT) behaves as a virtual twin of the real-world physical process. We present a deep neural operator enabled computational framework of the DT for closed-loop feedback control of the L-PBF process. The developed DT is envisioned to guide the AM process and facilitate high-quality manufacturing.
  arXiv  Detail & Related papers  (2024-05-13T03:53:46Z)
• Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows [43.148818844265236]
  Training scientific machine learning (SciML) models on limited high-fidelity data offers one approach to rapidly predict behaviors for situations that have not been seen before. High-fidelity data is itself in limited quantity to validate all outputs of the SciML model in unexplored input space. We extend a DeepONet using three different uncertainty mechanisms: mean-variance estimation, evidential uncertainty, and ensembling.
  arXiv  Detail & Related papers  (2023-10-31T18:07:29Z)
• Forecasting subcritical cylinder wakes with Fourier Neural Operators [58.68996255635669]
  We apply a state-of-the-art operator learning technique to forecast the temporal evolution of experimentally measured velocity fields. We find that FNOs are capable of accurately predicting the evolution of experimental velocity fields throughout the range of Reynolds numbers tested.
  arXiv  Detail & Related papers  (2023-01-19T20:04:36Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)

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.