High-velocity impact study of an advanced ceramic using finite element model coupling with a machine learning approach

Yang, Alex; Romanyk, Dan L.; Hogan, James D.

doi:10.1016/j.ceramint.2022.11.234

Cited by 7 publications

(4 citation statements)

References 76 publications

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“…The results show that the stiffness decreases from 662 ± 14.3 GPa to 310 ± 11.7 GPa in Figure 4b, but the ultimate fracture stress (42.8 ± 3.8 GPa to 50.7 ± 4.4 GPa) and strain (0.08 ± 0.002 to 0.24 ± 0.002) values increase with increasing strain rates in Figure 4c. Altogether, these results on the rate-dependent properties serve to feed higher-scale models [13] towards better predicting the mechanical response of alumina ceramics under impact and shock loading [57].…”

Section: Effects Of Strain Rate On Crack Properties Under Triaxial Lo...mentioning

confidence: 93%

Molecular Dynamics Simulations Correlating Mechanical Property Changes of Alumina with Atomic Voids under Triaxial Tension Loading

2023

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The functionalization of nanoporous ceramics for applications in healthcare and defence necessitates the study of the effects of geometric structures on their fundamental mechanical properties. However, there is a lack of research on their stiffness and fracture strength along diverse directions under multi-axial loading conditions, particularly with the existence of typical voids in the models. In this study, accurate atomic models and corresponding properties were meticulously selected and validated for further investigation. Comparisons were made between typical material geometric and elastic properties with measured results to ensure the reliability of the selected models. The mechanical behavior of nanoporous alumina under multiaxial stretching was explored through molecular dynamics simulations. The results indicated that the stiffness of nanoporous alumina ceramics under uniaxial tension was greater, while the fracture strength was lower compared to that under multiaxial loading. The fracture of nanoporous ceramics under multi-axial stretching, was mainly dominated by void and crack extension, atomic bond fracture, and cracking with different orientations. Furthermore, the effects of increasing strain rates on the void volume fraction were found to be similar across different initial radii. It was also found that the increasing tension loading rates had greater effects on decreasing the fracture strain. These findings provide additional insight into the fracture mechanisms of nanoporous ceramics under complex loading states, which can also contribute to the development of higher-scale models in the future.

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Section: Effects Of Strain Rate On Crack Properties Under Triaxial Lo...mentioning

confidence: 93%

Molecular Dynamics Simulations Correlating Mechanical Property Changes of Alumina with Atomic Voids under Triaxial Tension Loading

2023

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“…Traditional methods for predicting the elastic properties of materials rely on empirical relationships, such as Hooke's law, which are based on experimental data and generally provide reasonable estimates for well-established materials [17]. However, these methods can be limited when applied to extreme conditions (e.g., very low or high temperatures [18], high pressure [19], and corrosive environments [20]), as well as for complex materials (e.g., advanced ceramics [21,22] and composites [23,24]). The elastic properties reported for boron carbide typically originate from room-temperature experimental setups.…”

Section: Introductionmentioning

confidence: 99%

Temperature-Dependent Elastic Properties of B4C from First-Principles Calculations and Phonon Modeling

Sheikhi,

Stroberg,

Hogan

2024

Ceramics

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Boron carbide plays a crucial role in various extreme environment applications, including thermal barrier coatings, aerospace applications, and neutron absorbers, because of its high thermal and chemical stability. In this study, the temperature-dependent elastic stiffness constants, thermal expansion coefficient, Helmholtz free energy, entropy, and heat capacity at a constant volume (Cv) of rhombohedral B4C have been predicted using a quasi-harmonic approach. A combination of volume-dependent first-principles calculations (density functional theory) and first-principles phonon calculations in the supercell framework has been performed. Good agreement between the elastic constants and structural parameters from static calculations is observed. The calculated thermodynamic properties from phonon calculations show trends that align with the literature. As the temperature rises, the predicted free energy follows a decreasing trend, while entropy and Cv follow increasing trends with temperature. Comparisons between the predicted room temperature thermal expansion coefficient (TEC) (7.54×10−6 K−1) and bulk modulus (228 GPa) from the quasi-harmonic approach and literature results from experiments and models are performed, revealing that the calculated TEC and bulk modulus fall within the established range from the limited set of data from the literature (TEC = 5.73–9.50 ×10−6 K−1, B = 221–246 GPa). Temperature-dependent Cijs are predicted, enabling stress analysis at elevated temperatures. Overall, the outcomes of this study can be used when performing mechanical and thermal stress analysis (e.g., space shielding applications) and optimizing the design of boron carbide materials for elevated temperature applications.

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“…Artificial intelligence, particularly ML, provides the ability to search for optimized solutions and validate anticipated results [27] by leveraging training and test data to generate results that closely align with ground truth [28]. Accordingly, deep learning neural network algorithms have found applications in various fields, including robotics [29], structural health monitoring [30], and material sciences [31]. Within this context, convolutional neural networks (CNNs) [32] have emerged as a groundbreaking approach in the field of deep learning and have proven transformative in numerous engineering domains [33,34].…”

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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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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High-velocity impact study of an advanced ceramic using finite element model coupling with a machine learning approach

Cited by 7 publications

References 76 publications

Molecular Dynamics Simulations Correlating Mechanical Property Changes of Alumina with Atomic Voids under Triaxial Tension Loading

Molecular Dynamics Simulations Correlating Mechanical Property Changes of Alumina with Atomic Voids under Triaxial Tension Loading

Temperature-Dependent Elastic Properties of B4C from First-Principles Calculations and Phonon Modeling

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

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