journal article·2025-01

Three-dimensional cohesive finite element simulations coupled with machine learning to predict mechanical properties of polymer-bonded explosives

Daokun Lu, Bingru Zhang, Liu Liu, Haitao Zhang, Luoxia Cao, Yang Zhou

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OpenAlex topics: Energetic Materials and Combustion · High-Velocity Impact and Material Behavior · Injection Molding Process and Properties

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References (38)

Show source-provided references
1. Teipel · 2004
2. Zukas · 1998
3. Temperature and strain rate effects on the mechanical properties of a polymer-bonded explosive10.1140/epjst/e2018-00060-6 ↗
4. Three-dimensional cohesive finite element simulations of mechanical behavior of polymer-bonded explosives under quasi-static tensile loading10.1002/prep.202300177 ↗
5. Effects of fluoropolymer binders on the mechanical properties of TATB-based PBX10.1002/prep.201700295 ↗
6. Predicting the mechanical behaviour of highly particle-filled polymer composites using the nonlinear finite element method10.1016/j.compstruct.2022.115275 ↗
7. Construction of self-reinforced polymer based energetic composites with nano-energetic crystals to enhance mechanical properties10.1016/j.compositesa.2021.106604 ↗
8. Bioinspired interfacial reinforcement of polymer-based energetic composites with a high loading of solid explosive crystals10.1039/C7TA03424E ↗
9. Quasi-static tension-compression nonlinear constitutive model of TATB-based PBX and its application
10. Numerical simulation of crack initiation and growth in PBX high explosive subject to compression
11. Modelling the damage and deformation process in a plastic bonded explosive microstructure under tension using the finite element method10.1016/j.commatsci.2015.08.004 ↗
12. Tree-based machine learning approach to modelling tensile strength retention of Fibre Reinforced Polymer composites exposed to elevated temperatures10.1016/j.compositesb.2023.111132 ↗
13. Machine-learning-assisted multiscale modeling strategy for predicting mechanical properties of carbon fiber reinforced polymers10.1016/j.compscitech.2024.110455 ↗
14. Considering computational speed vs. accuracy: choosing appropriate mesoscale RVE boundary conditions10.1016/j.cma.2020.113572 ↗
15. Imposing periodic boundary condition on arbitrary meshes by polynomial interpolation10.1016/j.commatsci.2011.10.017 ↗
16. Synthesis of fluoropolymer containing tunable unsaturation by a controlled dehydrochlorination of P(VDF-co-CTFE) and its curing for high performance rubber applications10.1039/c2jm33133k ↗
17. Improving deep learning with generic data augmentation
18. Root mean square error (RMSE) or mean absolute error (MAE) – arguments against avoiding RMSE in the literature10.5194/gmd-7-1247-2014 ↗
19. Applying machine learning techniques to predict the properties of energetic materials10.1038/s41598-018-27344-x ↗
20. Fracture model for cemented aggregates, AIP
21. Meso-scale damage simulation of 3D braided composites under quasi-static axial tension10.1007/s10443-016-9579-z ↗
22. Research on the fracture behavior of PBX under static tension10.1016/j.dt.2014.05.010 ↗
23. A progressive damage model for quasi-static tension of 2D woven composites and FEM implementation10.1016/j.compstruct.2023.117168 ↗
24. Cunningham · 2007
25. Uniaxial quasi-static stress-strain relationship of TATB-based PBX based on SVM algorithm
26. Quasistatic anisotropic induced behaviour of a tatb-based plastic-bonded explosive10.1016/j.mechmat.2022.104318 ↗
27. Osborn · 1982
28. Damage behavior of high particle volume fraction composites with initial damage by finite element meso-modeling10.1016/j.compstruct.2023.117785 ↗
29. Constructing bioinspired hierarchical structure in polymer based energetic composites with superior thermal conductivity10.1016/j.compositesb.2019.01.046 ↗
30. Molecular dynamics investigation of adhesion between TATB surfaces and amorphous fluoropolymers10.1021/ma0702501 ↗
31. Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning10.1038/s41598-019-50144-w ↗
32. Machine learning assisted prediction of mechanical properties of graphene/aluminium nanocomposite based on molecular dynamics simulation10.1016/j.matdes.2021.110334 ↗
33. A survey on image data augmentation for deep learning10.1186/s40537-019-0197-0 ↗
34. Synthesising realistic 2D microstructures of unidirectional fibre-reinforced composites with a generative adversarial network10.1016/j.compscitech.2024.110539 ↗
35. The unreasonable effectiveness of deep features as a perceptual metric
36. Ensemble machine learning for predicting the homogenized elastic properties of unidirectional composites: a SHAP-based interpretability analysis10.1007/s10409-023-23301-x ↗
37. Machine learning-based heat deflection temperature prediction and effect analysis in polypropylene composites using catboost and shapley additive explanations10.1016/j.engappai.2023.106873 ↗
38. Investigation into durable polymers with enhanced toughness and elasticity for application in flexible Li-ion batteries10.1021/acsaem.0c02442 ↗