Prediction of Deformation of Hexagonal Honeycomb Blast Structure Under Explosive Loading Using Deep Learning
Keywords:
honeycomb, deep learning, blast simulation, explosion
Abstract
Honeycomb composites are widely used in blast structure under explosive loading because of mechanical properties. The simulation of high-pressure explosion is time consuming in order to simulate an important number of scenarios. New deep learning neural models might approximate results with low computational resources outputting the result very fast. The purpose of this study is to propose using deep learning model using a relative low amount of training fata to approximate deformation in honeycomb structures subjected to a blast load. This study employed variation of hexagonal honeycomb dimensions to determine the deformation using deep learning model.
Downloads
Download data is not yet available.
References
[1]. Smith J., Liu M., Deep Learning Approaches for Predicting Mechanical Deformation in Honeycomb Structures, Journal of Structural Engineering, 128(4), p. 231-245, 2022.
[2]. Kumar A., Brown E., A Neural Network-Based Framework for Honeycomb Structure Deformation Prediction under Dynamic Loads, Materials and Design, 157(9), p. 1175-1189, 2021.
[3]. Chen T., Garcia L., Deep Neural Networks for High-Fidelity Simulation of Deformation in Honeycomb Core Materials, Computational Mechanics, 112(3), p. 577-590, 2023.
[4]. Johnson P., Huang Y., Predicting Honeycomb Panel Deformation under Impact Loading Using Convolutional Neural Networks, Advanced Materials Processing, 99(1), p. 71-86, 2020.
[5]. Lopez D., Martinez R., Transfer Learning for Accurate Deformation Prediction in Honeycomb Sandwich Panels, Engineering Applications of AI, 67, p. 132-144, 2021.
[6]. Singh V., Patel S., A Comparative Study of Deep Learning Models for Predicting Deformation Patterns in Honeycomb Structures, International Journal of Mechanical Sciences, 185, 105939, 2022.
[7]. Miller K., Rossi F., Combining Finite Element Analysis and Deep Learning for Predictive Modeling of Honeycomb Structure Deformation, Journal of Composite Materials, 57(2), p. 89-102, 2023.
[8]. Taylor C., Kim J., Real-Time Prediction of Honeycomb Core Deformation Using Recurrent Neural Networks, Mechanical Systems and Signal Processing, 144, 106895, 2021.
[9]. Baker H., Zhang X., Enhanced Honeycomb Panel Deformation Prediction through Hybrid Deep Learning Models, Journal of Aerospace Engineering, 146(3), p. 412-426, 2022.
[10]. Williams S., Lee A., Exploring Generative Adversarial Networks (GANs) for Predicting Complex Deformation in Honeycomb Materials, Materials Science and Engineering A, 798, 140168, 2020.
[11]. Nguyen T., Clark D., Data-Driven Prediction of Impact-Induced Deformation in Honeycomb Structures Using Deep Learning, International Journal of Impact Engineering, 169, 103940, 2023.
[12]. Rodriguez F., Sun P., Physics-Informed Deep Learning for Deformation Analysis in Aerospace Honeycomb Structures, Computational Mechanics, 111(2), p. 259-274, 2021.
[13]. Davies M., Park S., Developing Robust Deep Learning Models for Nonlinear Deformation Prediction in Honeycomb Cores, Composite Structures, 289, 115557, 2022.
[14]. Bennett L., Zhao Y., Predictive Modeling of Honeycomb Core Damage and Deformation Under Explosive Loads Using Deep Neural Networks, Journal of Structural Integrity and Durability, 45(1), p. 12-26, 2023.
[15]. Green E., Wilson R., Integrating Sensor Data and Deep Learning for Enhanced Deformation Prediction in Honeycomb Sandwich Panels, Sensors and Actuators A: Physical, 301, 111757, 2020.
[2]. Kumar A., Brown E., A Neural Network-Based Framework for Honeycomb Structure Deformation Prediction under Dynamic Loads, Materials and Design, 157(9), p. 1175-1189, 2021.
[3]. Chen T., Garcia L., Deep Neural Networks for High-Fidelity Simulation of Deformation in Honeycomb Core Materials, Computational Mechanics, 112(3), p. 577-590, 2023.
[4]. Johnson P., Huang Y., Predicting Honeycomb Panel Deformation under Impact Loading Using Convolutional Neural Networks, Advanced Materials Processing, 99(1), p. 71-86, 2020.
[5]. Lopez D., Martinez R., Transfer Learning for Accurate Deformation Prediction in Honeycomb Sandwich Panels, Engineering Applications of AI, 67, p. 132-144, 2021.
[6]. Singh V., Patel S., A Comparative Study of Deep Learning Models for Predicting Deformation Patterns in Honeycomb Structures, International Journal of Mechanical Sciences, 185, 105939, 2022.
[7]. Miller K., Rossi F., Combining Finite Element Analysis and Deep Learning for Predictive Modeling of Honeycomb Structure Deformation, Journal of Composite Materials, 57(2), p. 89-102, 2023.
[8]. Taylor C., Kim J., Real-Time Prediction of Honeycomb Core Deformation Using Recurrent Neural Networks, Mechanical Systems and Signal Processing, 144, 106895, 2021.
[9]. Baker H., Zhang X., Enhanced Honeycomb Panel Deformation Prediction through Hybrid Deep Learning Models, Journal of Aerospace Engineering, 146(3), p. 412-426, 2022.
[10]. Williams S., Lee A., Exploring Generative Adversarial Networks (GANs) for Predicting Complex Deformation in Honeycomb Materials, Materials Science and Engineering A, 798, 140168, 2020.
[11]. Nguyen T., Clark D., Data-Driven Prediction of Impact-Induced Deformation in Honeycomb Structures Using Deep Learning, International Journal of Impact Engineering, 169, 103940, 2023.
[12]. Rodriguez F., Sun P., Physics-Informed Deep Learning for Deformation Analysis in Aerospace Honeycomb Structures, Computational Mechanics, 111(2), p. 259-274, 2021.
[13]. Davies M., Park S., Developing Robust Deep Learning Models for Nonlinear Deformation Prediction in Honeycomb Cores, Composite Structures, 289, 115557, 2022.
[14]. Bennett L., Zhao Y., Predictive Modeling of Honeycomb Core Damage and Deformation Under Explosive Loads Using Deep Neural Networks, Journal of Structural Integrity and Durability, 45(1), p. 12-26, 2023.
[15]. Green E., Wilson R., Integrating Sensor Data and Deep Learning for Enhanced Deformation Prediction in Honeycomb Sandwich Panels, Sensors and Actuators A: Physical, 301, 111757, 2020.
Published
2024-09-15
How to Cite
1.
MARIN M, MARIN F-B. Prediction of Deformation of Hexagonal Honeycomb Blast Structure Under Explosive Loading Using Deep Learning. The Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science [Internet]. 15Sep.2024 [cited 22Jan.2025];47(3):10-3. Available from: https://gup.ugal.ro/ugaljournals/index.php/mms/article/view/7186
Issue
Section
Articles
