An official website of the United States government
Understanding thermal stress evolution in metal additive manufacturing (AM) is crucial for producing high-quality components. Recent advancements in machine learning (ML) have shown great potential for modeling complex multiphysics problems in metal AM. While physics-based simulations face the challenge of high computational costs, conventional data-driven ML models require large, labeled training datasets to achieve accurate predictions. Unfortunately, generating large datasets for ML model training through time-consuming experiments or high-fidelity simulations is highly expensive in metal AM. To address these challenges, 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 during the laser metal deposition (LMD) process. The study also discusses enhanced accuracy and efficiency of the PINN model when supplemented with small simulation data. Furthermore, it highlights the PINN transferability, enabling fast predictions with a set of new process parameters using a pre-trained PINN model as an online soft sensor, significantly reducing computation time compared to physics-based numerical models while maintaining accuracy.
more »
« less
A Physics-Informed Convolutional Neural Network with Custom Loss Functions for Porosity Prediction in Laser Metal Deposition
Physics-informed machine learning is emerging through vast methodologies and in various applications. This paper discovers physics-based custom loss functions as an implementable solution to additive manufacturing (AM). Specifically, laser metal deposition (LMD) is an AM process where a laser beam melts deposited powder, and the dissolved particles fuse to produce metal components. Porosity, or small cavities that form in this printed structure, is generally considered one of the most destructive defects in metal AM. Traditionally, computer tomography scans measure porosity. While this is useful for understanding the nature of pore formation and its characteristics, purely physics-driven models lack real-time prediction ability. Meanwhile, a purely deep learning approach to porosity prediction leaves valuable physics knowledge behind. In this paper, a hybrid model that uses both empirical and simulated LMD data is created to show how various physics-informed loss functions impact the accuracy, precision, and recall of a baseline deep learning model for porosity prediction. In particular, some versions of the physics-informed model can improve the precision of the baseline deep learning-only model (albeit at the expense of overall accuracy).
more »
« less
- Award ID(s):
- 1852215
- PAR ID:
- 10319804
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 22
- Issue:
- 2
- ISSN:
- 1424-8220
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Abstract Laser-based additive manufacturing (LBAM) provides unrivalled design freedom with the ability to manufacture complicated parts for a wide range of engineering applications. Melt pool is one of the most important signatures in LBAM and is indicative of process anomalies and part defects. High-speed thermal images of the melt pool captured during LBAM make it possible for in situ melt pool monitoring and porosity prediction. This paper aims to broaden current knowledge of the underlying relationship between process and porosity in LBAM and provide new possibilities for efficient and accurate porosity prediction. We present a deep learning-based data fusion method to predict porosity in LBAM parts by leveraging the measured melt pool thermal history and two newly created deep learning neural networks. A PyroNet, based on Convolutional Neural Networks, is developed to correlate in-process pyrometry images with layer-wise porosity; an IRNet, based on Long-term Recurrent Convolutional Networks, is developed to correlate sequential thermal images from an infrared camera with layer-wise porosity. Predictions from PyroNet and IRNet are fused at the decision-level to obtain a more accurate prediction of layer-wise porosity. The model fidelity is validated with LBAM Ti–6Al–4V thin-wall structure. This is the first work that manages to fuse pyrometer data and infrared camera data for metal additive manufacturing (AM). The case study results based on benchmark datasets show that our method can achieve high accuracy with relatively high efficiency, demonstrating the applicability of the method for in situ porosity detection in LBAM.more » « less
-
K. Ellis, W. Ferrell (Ed.)Fused deposition modeling (FDM) is one of the widely used additive manufacturing (AM) processes but shares major shortcomings typical due to its layer-by-layer fabrication. These challenges (poor surface finishes, presence of pores, inconsistent mechanical properties, etc.) have been attributed to FDM input process parameters, machine parameters, and material properties. Deep learning, a type of machine learning algorithm has proven to help reveal complex and nonlinear input-output relationships without the need for the underlying physics. This research explores the power of multilayer perceptron deep learning algorithm to create a prediction model for critical input process parameters (layer thickness, extrusion temperature, build temperature, build orientation, and print speed) to predict three functional output parameters (dimension accuracy, porosity, and tensile strength) of FDM printed part. A fractional factorial design of experiment was performed and replicated three times per run (n=3). The number of neurons for the hidden layers, learning rate, and epoch were varied. The computational run time, loss function, and root mean square error (RMSE) were used to select the best prediction model for each FDM output parameter. The findings of this work are being extended to online monitoring and real-time control of the AM process enabling an AM digital twin.more » « less
-
K. Ellis, W. Ferrell (Ed.)Fused deposition modeling (FDM) is one of the widely used additive manufacturing (AM) processes but shares major shortcomings typical due to its layer-by-layer fabrication. These challenges (poor surface finishes, presence of pores, inconsistent mechanical properties, etc.) have been attributed to FDM input process parameters, machine parameters, and material properties. Deep learning, a type of machine learning algorithm has proven to help reveal complex and nonlinear input-output relationships without the need for the underlying physics. This research explores the power of multilayer perceptron deep learning algorithm to create a prediction model for critical input process parameters (layer thickness, extrusion temperature, build temperature, build orientation, and print speed) to predict three functional output parameters (dimension accuracy, porosity, and tensile strength) of FDM printed part. A fractional factorial design of experiment was performed and replicated three times per run (n=3). The number of neurons for the hidden layers, learning rate, and epoch were varied. The computational run time, loss function, and root mean square error (RMSE) were used to select the best prediction model for each FDM output parameter. The findings of this work are being extended to online monitoring and real-time control of the AM process enabling an AM digital twin.more » « less
-
Solar wind modeling is classified into two main types: empirical models and physics-based models, each designed to forecast solar wind properties in various regions of the heliosphere. Empirical models, which are cost-effective, have demonstrated significant accuracy in predicting solar wind at the L1 Lagrange point. On the other hand, physics-based models rely on magnetohydrodynamics (MHD) principles and demand more computational resources. In this research paper, we build upon our recent novel approach that merges empirical and physics-based models. Our recent proposal involves the creation of a new physics-informed neural network that leverages time series data from solar wind predictors to enhance solar wind prediction. This innovative method aims to combine the strengths of both modeling approaches to achieve more accurate and efficient solar wind predictions. In this work, we show the variability of the proposed physics-informed loss across multiple deep learning models. We also study the effect of training the models on different solar cycles on the model’s performance. This work represents the first effort to predict solar wind by integrating deep learning approaches with physics constraints and analyzing the results across three solar cycles. Our findings demonstrate the superiority of our physics-constrained model over other unconstrained deep learning predictive models.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s22020494
