journal article·2022-11

Mesh/membrane composite with superior mechanical performance: A deep learning-based design

Yunce Zhang, Qiang Tao, Yuanpeng Liu, Changguo Wang

Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Modular Robots and Swarm Intelligence · Innovations in Concrete and Construction Materials · Cellular and Composite Structures

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (51)

Show source-provided references
1. Wrinkling and collapse of mesh reinforced membrane inflated beam under bending10.1016/j.actaastro.2016.08.021 ↗
2. Tensile–tearing analysis of rectangular thin film with central defect
3. Progress of super-pressure balloon development: A new “tawara” concept with improved stability10.1016/j.asr.2011.05.008 ↗
4. M. Pagitz, S. Pellegrino, Computation of buckling pressure of pumpkin balloons, in: 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference 14th AIAA/ASME/AHS Adaptive Structures Conference 7th, 2006, p. 1803.10.2514/6.2006-1803 ↗
5. J. Hinkle, J. Lin, J. Watson, Deployment testing of an expandable lunar habitat, in: AIAA SPACE 2009 Conference & Exposition, 2009, p. 6447.10.2514/6.2009-6447 ↗
6. Pressure-induced instability and its coupled aeroelasticity of inflated pillow10.1016/j.cja.2021.02.015 ↗
7. Buckling pressure of “pumpkin” balloons10.1016/j.ijsolstr.2007.03.021 ↗
8. M. Pagitz, S. Pellegrino, Shape optimization of” pumpkin” balloons, in: AIAA Balloon Systems Conference, 2007, p. 2606.10.2514/6.2007-2606 ↗
9. Y. Xu, M. Chen, W. Chen, Buckling and Post-buckling of a Pumpkin Balloon, in: 51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference 18th AIAA/ASME/AHS Adaptive Structures Conference 12th, 2010, p. 2671.10.2514/6.2010-2671 ↗
10. Development of a super-pressure balloon with an improved design
11. Development of a super-pressure balloon with a diamond-shaped net10.1016/j.asr.2014.06.043 ↗
12. K. Nakashino, Y. Saito, K. Goto, D. Akita, T. Matsuo, K. Matsushima, H. Hashimoto, S. Shimadu, Super pressure balloon with diamond-shaped net: A numerical study of its structural characteristics, in: 4th AIAA Spacecraft Structures Conference, 2017, p. 1117.10.2514/6.2017-1117 ↗
13. Advanced honeycomb designs for improving mechanical properties: A review10.1016/j.compositesb.2021.109393 ↗
14. Geometric imperfection effects in an elastically deployable isogrid column10.2514/2.3887 ↗
15. R. Allred, A. Hoyt, S. Scarborough, D. Cadogan, P. McElroy, UV rigidizable carbon-reinforced isogrid inflatable booms, in: 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2002, p. 1202.10.2514/6.2002-1202 ↗
16. Discrete analysis of stiffened composite cylindrical shells10.2514/3.9162 ↗
17. Buckling analysis of composite hexagonal lattice cylindrical shell using smeared stiffener model10.14429/dsj.59.1516 ↗
18. Buckling analysis of composite lattice sandwich shells under uniaxial compression based on the effective analytical equivalent approach10.1016/j.compositesb.2019.106932 ↗
19. Buckling load prediction of grid-stiffened composite cylindrical shells using the vibration correlation technique10.1016/j.compscitech.2018.08.046 ↗
20. Anisogrid composite lattice structures–Development and aerospace applications10.1016/j.compstruct.2011.10.023 ↗
21. Inverse molecular design using machine learning: Generative models for matter engineering10.1126/science.aat2663 ↗
22. Mechanomaterials: A rational deployment of forces and geometries in programming functional materials
23. Data-driven computation for history-dependent materials10.1016/j.crme.2019.11.008 ↗
24. Machine learning integrated design for additive manufacturing
25. A machine learning workflow for 4D printing: understand and predict morphing behaviors of printed active structures10.1088/1361-665X/abc836 ↗
26. Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment10.1039/C8MH00653A ↗
27. Designing phononic crystal with anticipated band gap through a deep learning based data-driven method10.1016/j.cma.2019.112737 ↗
28. Accelerating auxetic metamaterial design with deep learning10.1002/adem.202070018 ↗
29. Generative deep neural networks for inverse materials design using backpropagation and active learning10.1002/advs.201902607 ↗
30. De novo composite design based on machine learning algorithm10.1016/j.eml.2017.10.001 ↗
31. Machine learning-based design and optimization of curved beams for multistable structures and metamaterials10.1016/j.eml.2020.101002 ↗
32. Machine-learning optimized method for regional control of sound fields10.1016/j.eml.2021.101297 ↗
33. Exploring the 3D architectures of deep material network in data-driven multiscale mechanics10.1016/j.jmps.2019.03.004 ↗
34. End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures10.1016/j.jmps.2021.104506 ↗
35. Prediction of two-phase composite microstructure properties through deep learning of reduced dimensional structure-response data
36. On the potential of recurrent neural networks for modeling path dependent plasticity10.1016/j.jmps.2020.103972 ↗
37. Deep learning predicts path-dependent plasticity10.1073/pnas.1911815116 ↗
38. A methodology to generate design allowables of composite laminates using machine learning10.1016/j.ijsolstr.2021.111095 ↗
39. A parametric study of adhesive bonded joints with composite material using black-box and grey-box machine learning methods: Deep neuron networks and genetic programming10.1016/j.compositesb.2021.108894 ↗
40. Humfeld · 2021
41. 2013 Koiter medal paper: crack-tip fields and toughness of two-dimensional elastoplastic lattices10.1115/1.4030666 ↗
42. Fracture toughness of two-dimensional cellular material with periodic microstructure10.1007/s10704-007-9171-5 ↗
43. Recent trends in deep learning based natural language processing10.1109/MCI.2018.2840738 ↗
44. Learning long-term dependencies with gradient descent is difficult10.1109/72.279181 ↗
45. Long short-term memory10.1162/neco.1997.9.8.1735 ↗
46. Chung · 2014
47. Bahdanau · 2014
48. Keras · 2021
49. Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature10.5194/gmd-7-1247-2014 ↗
50. Activation functions in neural networks
51. Agarap · 2018