SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Physical Fields in Metal Forming Numerical Simulation

Xu, Qiwei; Nie, Zhenguo; Xu, Handing; Zhou, Haosu; Attar, Hamid Reza; Li, Nan; Xie, Fugui; Liu, Xin-Jun

doi:10.1115/1.4052195

Cited by 14 publications

(3 citation statements)

References 39 publications

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“…This model is used to produce meshes automatically during the finite element method (FEM) computation process. Although this does not save time, it increases computing productivity [ 105 ]. Figure 13 shows the network structure of Residual MeshNet.…”

Section: Object Reconstructionmentioning

confidence: 99%

Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper

Vinodkumar,

Karabulut,

Avots

et al. 2024

Entropy

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The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artificial intelligence for addressing computer vision challenges. However, deep learning on three-dimensional data presents distinct obstacles and is now in its nascent phase. There have been significant advancements in deep learning specifically for three-dimensional data, offering a range of ways to address these issues. This study offers a comprehensive examination of the latest advancements in deep learning methodologies. We examine many benchmark models for the tasks of 3D object registration, augmentation, and reconstruction. We thoroughly analyse their architectures, advantages, and constraints. In summary, this report provides a comprehensive overview of recent advancements in three-dimensional deep learning and highlights unresolved research areas that will need to be addressed in the future.

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Section: Object Reconstructionmentioning

confidence: 99%

Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper

Vinodkumar,

Karabulut,

Avots

et al. 2024

Entropy

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“…Sepasdar et al [22] constructed a two-stacked generator CNN framework to predict complete field damage and failure pattern prediction in composite materials. To enhance deep learning models' precision and training cost, Xu et al [23] increased the computational efficiency of low-mesh density FEM models by developing a data-driven mesh density boosting model that uses the low mesh-density physical field as inputs to predict high-density physical field as outputs. A Fourier neural operator (FNO) was used by Rashid et al [24] to accurately predict and design the mechanical responses of complex 2D composite microstructures, demonstrating high-fidelity stress and strain predictions with few training data and showing zero-shot generalization and super-resolution abilities, even for unseen geometries and low-resolution inputs.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Techniques for Predicting Stress Fields in Composite Materials: A Superior Alternative to Finite Element Analysis

et al. 2023

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Stress evaluation plays a pivotal role in the design of material systems, often accomplished through the finite element method (FEM) for intricate structures. However, the substantial costs and time requirements associated with multi-scale FEM analyses have prompted a growing interest in adopting more efficient, machine-learning-driven strategies. This study investigates the utilization of advanced machine learning techniques for predicting local stress fields in composite materials, presenting it as a superior alternative to traditional FEM approaches. The primary objective of this research is to develop a predictive model for stress field maps in composite components featuring diverse configurations of fibers distributed within the matrix. To achieve this, we employ a Convolutional Neural Network (CNN) with a specialized U-Net architecture, enabling the correlation of spatial fiber organization with the resultant von Mises stress field. The CNN model was extensively trained using four distinct data sets, encompassing uniform fibrous structures, non-uniform fibrous structures, irregularly shaped fibrous structures, and a comprehensive combination of these data sets. The trained U-Net models demonstrate exceptional proficiency in predicting von Mises stress fields, yielding impressive structural similarity index scores (SSIM) of 0.977 and mean squared errors (MSE) of 0.0009 on a dedicated test set. This research harnesses 2D cross-sectional imagery to establish a surrogate model for finite element analysis, offering an accurate and efficient approach for predicting stress fields in composite material design, irrespective of geometric complexity or boundary conditions.

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“…U-Net has been extended to several other fields beyond image segmentation, such as solving for the temperature field given a map of sources [28]. U-Net has also been used as a mesh super-resolution method for stress fields [25], where the output of a low resolution FE simulation is refined by a U-Net to match the output of a high resolution FE simulation without the required computation time and memory requirements.…”

Section: Introductionmentioning

confidence: 99%

Adapting U-Net for linear elastic stress estimation in polycrystal Zr microstructures

Langcaster¹,

Balint²,

Wenman³

2023

Preprint

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A variant of the U-Net convolutional neural network architecture is proposed to estimate linear elastic compatibility stresses in α-Zr (hcp) polycrystalline grain structures. Training data was generated using VGrain software with a regularity α of 0.73 and uniform random orientation for the grain structures and ABAQUS to evaluate the stress fields using the finite element method. The initial dataset contains 200 samples with 20 held from training for validation. The network gives speedups of around 200x to 6000x using a CPU or GPU, with significant memory savings, compared to finite element analysis with a modest reduction in accuracy of up to 10%. Network performance is not correlated with grain structure regularity or texture, showing generalisation of the network beyond the training set to arbitrary Zr crystal structures. Performance when trained with 200 and 400 samples was measured, finding an improvement in accuracy of approximately 10% when the size of the dataset was doubled.

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SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Physical Fields in Metal Forming Numerical Simulation

Cited by 14 publications

References 39 publications

Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper

Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper

Deep Learning Techniques for Predicting Stress Fields in Composite Materials: A Superior Alternative to Finite Element Analysis

Adapting U-Net for linear elastic stress estimation in polycrystal Zr microstructures

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