Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites

Gupta, Sristi; Mukhopadhyay, T.; Kushvaha, Vinod

doi:10.1016/j.dt.2022.09.008

Cited by 12 publications

(8 citation statements)

References 41 publications

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“…This is shown in figure 13(f) for the M-LD model in which there were no instances with an error higher than two, indicating a high level of accuracy and precision in the model's predictions compared to the results of the M-SD and M-MD models. In addition, shifting to the left trend with an increase in database size can be observed in the histograms, and a significantly higher portion of cases (88% of training data and 77% of test data) show an error near zero in the model with the biggest database size indicating a higher accuracy compared to similar studies from the literature [35][36][37].…”

Section: Effect Of the Dataset Sizementioning

confidence: 59%

“…In recent years, researchers have employed CNNs to predict the mechanical response of materials [35][36][37]. Hoq et al [38] predicted full-field stress responses in random heterogeneous materials using different data-driven methods, including classical ML techniques (artificial neural networks, random forest, and K-nearest neighbors), CNNs, and a modified conditional generative adversarial network (cGAN) [39].…”

Section: Introductionmentioning

confidence: 99%

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A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions

Rezasefat,

Hogan

2023

Mach. Learn.: Sci. Technol.

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This study presents a data-driven finite element-machine learning surrogate model for predicting the end-to-end full-field stress distribution and stress concentration around an arbitrary-shaped inclusion. This is important because the model’s capacity to handle large datasets, consider variations in size and shape, and accurately replicate stress fields makes it a valuable tool for studying how inclusion characteristics affect material performance. An automatized dataset generation method using finite element simulation is proposed, validated, and used for attaining a dataset with one thousand inclusion shapes motivated by experimental observations and their corresponding spatially-varying stress distributions. A U-Net-based convolutional neural network (CNN) is trained using the dataset, and its performance is evaluated through quantitative and qualitative comparisons. The dataset, consisting of these stress data arrays, is directly fed into the CNN model for training and evaluation. This approach bypasses the need for converting the stress data into image format, allowing for a more direct and efficient input representation for the CNN. The model was evaluated through a series of sensitivity analyses, focusing on the impact of dataset size and model resolution on accuracy and performance. The results demonstrated that increasing the dataset size significantly improved the model’s prediction accuracy, as indicated by the correlation values. Additionally, the investigation into the effect of model resolution revealed that higher resolutions led to better stress field predictions and reduced error. Overall, the surrogate model proved effective in accurately predicting the effective stress concentration in inclusions, showcasing its potential in practical applications requiring stress analysis such as structural engineering, material design, failure analysis, and multi-scale modeling.

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Section: Effect Of the Dataset Sizementioning

confidence: 59%

Section: Introductionmentioning

confidence: 99%

A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions

Rezasefat,

Hogan

2023

Mach. Learn.: Sci. Technol.

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“…In recent years, deep learning models and convolutional neural networks (CNNs) have emerged as powerful tools for field predictions in the domains of engineering and material science [1,2]. The potential of deep learning to uncover intricate patterns and spatial dependencies within complex datasets has enabled end-to-end field prediction outputs such as damage, stress, and strain from image datasets of material microstructures [3][4][5][6] or heterogeneous geometries [7][8][9][10]. Data-driven models trained using computer vision and semantic segmentation techniques [11] have utilized datasets with both paired [12,13] and unpaired [14,15] images from physics-informed simulation approaches like molecular dynamics (MD) [6,14] and finite element method (FEM) [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…While CNNs have exhibited promise in doing field predictions [3,13,14,16,17,34], the majority of existing CNN-based models have primarily concentrated on spatial information (limited to 2D with some recent 3D models showing promise [35][36][37]), often disregarding the integration of temporal aspects. This oversight in data dimension and time-evolution becomes especially apparent when considering the complexities of real-world systems, where temporal evolution plays a pivotal role in understanding mechanical behaviors [38].…”

Section: Introductionmentioning

confidence: 99%

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

Rezasefat,

Hogan

2024

Mach. Learn.: Sci. Technol.

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This study investigates the application of machine learning models to predict time-evolving stress fields in complex three-dimensional structures trained with full-scale finite element simulation data. Two novel architectures, the Multi-Decoder CNN (MUDE-CNN) and the Multiple Encoder-Decoder Model with Transfer Learning (MTED-TL), were introduced to address the challenge of predicting the progressive and spatial evolutional of stress distributions around defects. The MUDE-CNN leveraged a shared encoder for simultaneous feature extraction and employed multiple decoders for distinct time frame predictions, while MTED-TL progressively transferred knowledge from one encoder-decoder block to another, thereby enhancing prediction accuracy through transfer learning. These models were evaluated to assess their accuracy, with a particular focus on predicting temporal stress fields around an additive manufacturing-induced isolated pore, as understanding such defects is crucial for assessing mechanical properties and structural integrity in materials and components fabricated via additive manufacturing. The temporal model evaluation demonstrated MTED-TL's consistent superiority over MUDE-CNN, owing to transfer learning's advantageous initialization of weights and smooth loss curves. Furthermore, an autoregressive training framework was introduced to improve temporal predictions, consistently outperforming both MUDE-CNN and MTED-TL. By accurately predicting temporal stress fields around AM-induced defects, these models can enable real-time monitoring and proactive defect mitigation during the fabrication process. This capability ensures enhanced component quality and enhances the overall reliability of additively manufactured parts.

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“…A new model called StressGAN, a conditional generative adversarial network designed to predict 2D von Mises stress distributions in solid structures, was introduced by Jiang et al [16]. Gupta et al [17] utilized a pix2pix Convolutional CNN to predict the stress component S11 of fiber-reinforced polymer composites based on microstructural image inputs, demonstrating the model's robust predictive capabilities with a correlation score of 0.999 and L2 norm of less than 0.005. This approach allows for the expedited design and analysis of new composite materials, bypassing the need for labor-intensive numerical 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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Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites

Cited by 12 publications

References 41 publications

A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions

A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

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

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