DataVXApplied Composite MaterialsPaper
journal article·2024-06

An Efficient Optimization Method for Stacking Sequence of Composite Pressure Vessels Based on Artificial Neural Network and Genetic Algorithm

Jianguo Liang, Zemin Ning, Yinhui Li, Haifeng Gao, Jianglin Liu, Wang Tian, Xiaodong Zhao, Zhaotun Jia, Yuqin Xue, Chunxiang Miao

Applied Composite Materials· Volume 31· Issue 3
Publisher page
Bibliographic record

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

Composites

OpenAlex topics: Composite Structure Analysis and Optimization · Mechanical Engineering and Vibrations Research · Mechanical Behavior of Composites

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

References (69)

Show source-provided references
1. Chapelle, D., Perreux, D.: Optimal design of a type 3 hydrogen vessel: Part I—Analytic modelling of the cylindrical section. Int. J. Hydrog. Energy 31, 627–638 (2006). https://doi.org/10.1016/j.ijhydene.2005.06.01210.1016/j.ijhydene.2005.06.012 ↗
2. Sarkar, A.: A quantitative method for characterization of carbon nanotubes for hydrogen storage. Int. J. Hydrog. Energy 29, 1487–1491 (2004). https://doi.org/10.1016/j.ijhydene.2004.02.00310.1016/j.ijhydene.2004.02.003 ↗
3. Ross, D.K.: Hydrogen storage: The major technological barrier to the development of hydrogen fuel cell cars. Vacuum 80, 1084–1089 (2006). https://doi.org/10.1016/j.vacuum.2006.03.03010.1016/j.vacuum.2006.03.030 ↗
4. Brumfiel, G.: Hydrogen cars fuel debate on basic research. Nature 422, 104–104 (2003). https://doi.org/10.1038/422104a10.1038/422104a ↗
5. Takeichi, N.: “Hybrid hydrogen storage vessel”, a novel high-pressure hydrogen storage vessel combined with hydrogen storage material. Int. J. Hydrog. Energy (2003). https://doi.org/10.1016/S0360-3199(02)00216-110.1016/S0360-3199(02)00216-1 ↗
6. Xu, P., Zheng, J., Chen, H., Liu, P.: Optimal design of high pressure hydrogen storage vessel using an adaptive genetic algorithm. Int. J. Hydrog. Energy 35, 2840–2846 (2010). https://doi.org/10.1016/j.ijhydene.2009.05.00810.1016/j.ijhydene.2009.05.008 ↗
7. Mian, H.H., Wang, G., Dar, U.A., Zhang, W.: Optimization of Composite Material System and Lay-up to Achieve Minimum Weight pressure vessel. Appl. Compos. Mater. 20, 873–889 (2013). https://doi.org/10.1007/s10443-012-9305-410.1007/s10443-012-9305-4 ↗
8. Vafaeesefat, A., Khani, A.: Head shape and Winding Angle optimization of Composite pressure vessels based on a multi-level strategy. Appl. Compos. Mater. 14, 379–391 (2007). https://doi.org/10.1007/s10443-008-9052-810.1007/s10443-008-9052-8 ↗
9. Alcántar, V., Ledesma, S., Aceves, S.M., Ledesma, E., Saldaña, A.: Optimization of type III pressure vessels using genetic algorithm and simulated annealing. Int. J. Hydrog. Energy 42, 20125–20132 (2017). https://doi.org/10.1016/j.ijhydene.2017.06.14610.1016/j.ijhydene.2017.06.146 ↗
10. Alcántar, V., Aceves, S.M., Ledesma, E., Ledesma, S., Aguilera, E.: Optimization of type 4 composite pressure vessels using genetic algorithms and simulated annealing. Int. J. Hydrog. Energy 42, 15770–15781 (2017). https://doi.org/10.1016/j.ijhydene.2017.03.03210.1016/j.ijhydene.2017.03.032 ↗
11. Sabour, M.H., Foghani, M.F.: Design of semi-composite pressure vessel using fuzzy and FEM. Appl. Compos. Mater. 17, 175–182 (2010). https://doi.org/10.1007/s10443-009-9114-610.1007/s10443-009-9114-6 ↗
12. Lin, D.T.W., Hsieh, J.-C., Chindakham, N., Hai, P.D.: Optimal design of a composite laminate hydrogen storage vessel: Design of a composite laminate hydrogen storage vessel. Int. J. Energy Res. 37, 761–768 (2013). https://doi.org/10.1002/er.298310.1002/er.2983 ↗
13. Liu, P., Xu, P., Zheng, J.: Artificial immune system for optimal design of composite hydrogen storage vessel. Comput. Mater. Sci. 47, 261–267 (2009). https://doi.org/10.1016/j.commatsci.2009.07.01510.1016/j.commatsci.2009.07.015 ↗
14. Kim, S.-W., Kim, E.-H., Jeong, M.-S., Lee, I.: Damage evaluation and strain monitoring for composite cylinders using tin-coated FBG sensors under low-velocity impacts. Compos. Part B: Eng. 74, 13–22 (2015). https://doi.org/10.1016/j.compositesb.2015.01.00410.1016/j.compositesb.2015.01.004 ↗
15. Jiao, W., Niu, Y., Hao, L., Yang, F., Liu, W., Wang, R.: Optimal design of Lightweight Composite pressure vessel by using Artificial Immune Algorithm. Polym. Polym. Compos. 22, 323–328 (2014). https://doi.org/10.1177/09673911140220031510.1177/096739111402200315 ↗
16. Park, G., Jang, H., Kim, C.: Design of composite layer and liner for structure safety of hydrogen pressure vessel (type 4). J. Mech. Sci. Technol. 35, 3507–3517 (2021). https://doi.org/10.1007/s12206-021-0723-910.1007/s12206-021-0723-9 ↗
17. Paknahad, A., Fathi, A., Goudarzi, A.M., Nourani, R.: Optimum head design of filament wound composite pressure vessels using a hybrid model of FE analysis and inertia weight PSO algorithm. Int. J. Mater. Form 9, 49–57 (2016). https://doi.org/10.1007/s12289-014-1199-210.1007/s12289-014-1199-2 ↗
18. Vafaeesefat, A.: Dome shape optimization of Composite pressure vessels based on rational B-Spline curve and genetic algorithm. Appl. Compos. Mater. 16, 321–330 (2009). https://doi.org/10.1007/s10443-009-9097-310.1007/s10443-009-9097-3 ↗
19. Zhao, X., Liang, J., Liu, J., Feng, J., Qin, Z., Gao, H., Guo, Z., Jia, Z.: A novel multi-filament winding technique for type III composite pressure vessel: From CFRP cross-undulation concept to structural performance validation. Int. J. Hydrog. Energy 48, 17237–17250 (2023). https://doi.org/10.1016/j.ijhydene.2023.01.25810.1016/j.ijhydene.2023.01.258 ↗
20. Zhao, X., Liang, J., Zhao, C., Liu, J., Li, Y., Jiang, L., Feng, J., Xue, Y.: Experimental and numerical analysis of low-velocity impact behavior of wound products using multi-filament winding technique. J. Mater. Res. Technol. 25, 7292–7306 (2023). https://doi.org/10.1016/j.jmrt.2023.07.15910.1016/j.jmrt.2023.07.159 ↗
21. Nicholas, P.E., Padmanaban, K.P., Vasudevan, D., Ramachandran, T.: Stacking sequence optimization of horizontal axis wind turbine blade using FEA, ANN and GA. Struct. Multidisc Optim. 52, 791–801 (2015). https://doi.org/10.1007/s00158-015-1269-110.1007/s00158-015-1269-1 ↗
22. Bach, C., Jebari, R., Viti, A., Hewson, R.: Composite stacking sequence optimization for aeroelastically tailored forward-swept wings. Struct. Multidisc Optim. 55, 105–119 (2017). https://doi.org/10.1007/s00158-016-1477-310.1007/s00158-016-1477-3 ↗
23. Nagendra, S., Haftka, R.T., Gurdal, Z.: Stacking sequence optimization of simply supported laminates with stability and strain constraints. AIAA J. 30, 2132–2137 (1992). https://doi.org/10.2514/3.1119110.2514/3.11191 ↗
24. Kogiso, N., Watson, L.T., Gürdal, Z., Haftka, R.T.: Genetic algorithms with local improvement for composite laminate design. Struct. Optim. 7, 207–218 (1994). https://doi.org/10.1007/BF0174371410.1007/BF01743714 ↗
25. Khedmati, M.R., Sangtabi, M.R., Fakoori, M.: Stacking sequence optimisation of composite panels subjected to slamming impact loads using a genetic algorithm. Lat Am. J. Solids Struct. 10, 1043–1060 (2013). https://doi.org/10.1590/S1679-7825201300050001010.1590/S1679-78252013000500010 ↗
26. Ghiasi, H., Pasini, D., Lessard, L.: Optimum stacking sequence design of composite materials part I: Constant stiffness design. Compos. Struct. 90, 1–11 (2009). https://doi.org/10.1016/j.compstruct.2009.01.00610.1016/j.compstruct.2009.01.006 ↗
27. Lee, D.S., Morillo, C., Bugeda, G., Oller, S., Onate, E.: Multilayered composite structure design optimisation using distributed/parallel multi-objective evolutionary algorithms. Compos. Struct. 94, 1087–1096 (2012). https://doi.org/10.1016/j.compstruct.2011.10.00910.1016/j.compstruct.2011.10.009 ↗
28. Hong, H.-T., Chun, H.-J., Choi, H.-S.: Optimal strength design of composite bicycle wheels. Int. J. Precis. Eng. Manuf. 15, 1609–1613 (2014). https://doi.org/10.1007/s12541-014-0510-y10.1007/s12541-014-0510-y ↗
29. Bohrer, R.Z.G., Kim, I.Y.: Multi material topology and stacking sequence optimization of composite laminated plates. Struct. Multidisc Optim. 65, 274 (2022). https://doi.org/10.1007/s00158-022-03363-110.1007/s00158-022-03363-1 ↗
30. An, H., Chen, S., Huang, H.: Concurrent optimization of stacking sequence and stiffener layout of a composite stiffened panel. Eng. Optim. 51, 608–626 (2019). https://doi.org/10.1080/0305215X.2018.149257010.1080/0305215X.2018.1492570 ↗
31. Kalita, K., Dey, P., Haldar, S., Gao, X.-Z.: Optimizing frequencies of skew composite laminates with metaheuristic algorithms. Eng. Comput. 36, 741–761 (2020). https://doi.org/10.1007/s00366-019-00728-x10.1007/s00366-019-00728-x ↗
32. Pal, S., Kalita, K., Haldar, S.: Dynamic analysis of laminated shell panels with cutout and cracked corners carrying concentrated and distributed mass. Mech. Based Des. Struct. Mach. 1–28, 1 (2023). https://doi.org/10.1080/15397734.2023.220762610.1080/15397734.2023.2207626 ↗
33. Pal, S., Kalita, K., Majumdar, A., Haldar, S.: Optimization of frequency separation of laminated shells carrying transversely distributed mass using genetic algorithm. J. Vib. Control. 110775463231190270 (2023). https://doi.org/10.1177/1077546323119027710.1177/10775463231190277 ↗
34. Nebe, M., Asijee, T.J., Braun, C., Van Campen, J.M.J.F., Walther, F.: Experimental and analytical analysis on the stacking sequence of composite pressure vessels. Compos. Struct. 247, 112429 (2020). https://doi.org/10.1016/j.compstruct.2020.11242910.1016/j.compstruct.2020.112429 ↗
35. Riche, R.L., Haftka, R.T.: Optimization of laminate stacking sequence for buckling load maximization by genetic algorithm. AIAA J. 31, 951–956 (1993). https://doi.org/10.2514/3.1171010.2514/3.11710 ↗
36. Park, J.H., Hwang, J.H., Lee, C.S., Hwang, W.: Stacking sequence design of composite laminates for maximum strength using genetic algorithms. Compos. Struct. 52, 217–231 (2001). https://doi.org/10.1016/S0263-8223(00)00170-710.1016/S0263-8223(00)00170-7 ↗
37. Gürdal, Z., Haftka, R.T., Hajela, P.: Design and Optimization of Laminated Composite Materials. Wiley, New York (1999)
38. Soremekun, G., Gürdal, Z., Kassapoglou, C., Toni, D.: Stacking sequence blending of multiple composite laminates using genetic algorithms. Compos. Struct. 56, 53–62 (2002). https://doi.org/10.1016/S0263-8223(01)00185-410.1016/S0263-8223(01)00185-4 ↗
39. Lee, Y.-J., Lin, C.-C.: Regression of the response surface of laminated composite structures. Compos. Struct. 62, 91–105 (2003). https://doi.org/10.1016/S0263-8223(03)00095-310.1016/S0263-8223(03)00095-3 ↗
40. Vincenti, A., Vannucci, P., Ahmadian, M.R.: Optimization of laminated composites by using genetic algorithm and the Polar description of Plane Anisotropy. Mech. Adv. Mater. Struct. 20, 242–255 (2013). https://doi.org/10.1080/15376494.2011.56341510.1080/15376494.2011.563415 ↗
41. Iyengar, N.G.R., Vyas, N.: Optimum design of laminated composite under axial compressive load. Sadhana 36, 73–85 (2011). https://doi.org/10.1007/s12046-011-0009-510.1007/s12046-011-0009-5 ↗
42. Todoroki, A., Ishikawa, T.: Design of experiments for stacking sequence optimizations with genetic algorithm using response surface approximation. Compos. Struct. 64, 349–357 (2004). https://doi.org/10.1016/j.compstruct.2003.09.00410.1016/j.compstruct.2003.09.004 ↗
43. Nagendra, S., Haftka, R.T., Gürdal, Z.: Genetic algorithms for the design of Composite panels. In: Sih, G.C., Carpinteri, A., Surace, G. (eds.) Advanced Technology for Design and Fabrication of Composite Materials and Structures, pp. 129–143. Springer Netherlands, Dordrecht (1995)10.1007/978-94-015-8563-7_10 ↗
44. Messager, T., Pyrz, M., Gineste, B., Chauchot, P.: Optimal laminations of thin underwater composite cylindrical vessels. Compos. Struct. 58, 529–537 (2002). https://doi.org/10.1016/S0263-8223(02)00162-910.1016/S0263-8223(02)00162-9 ↗
45. Almeida, F.S., Awruch, A.M.: Design optimization of composite laminated structures using genetic algorithms and finite element analysis. Compos. Struct. 88, 443–454 (2009). https://doi.org/10.1016/j.compstruct.2008.05.00410.1016/j.compstruct.2008.05.004 ↗
46. Rajendran, I., Vijayarangan, S.: Optimal design of a composite leaf spring using genetic algorithms. Comput. Struct. 79, 1121–1129 (2001). https://doi.org/10.1016/S0045-7949(00)00174-710.1016/S0045-7949(00)00174-7 ↗
47. Gantovnik, V.B., Gürdal, Z., Watson, L.T.: A genetic algorithm with memory for optimal design of laminated sandwich composite panels. Compos. Struct. 58, 513–520 (2002). https://doi.org/10.1016/S0263-8223(02)00128-910.1016/S0263-8223(02)00128-9 ↗
48. Kalita, K., Mukhopadhyay, T., Dey, P., Haldar, S.: Genetic programming-assisted multi-scale optimization for multi-objective dynamic performance of laminated composites: The advantage of more elementary-level analyses. Neural Comput. Applic. 32, 7969–7993 (2020). https://doi.org/10.1007/s00521-019-04280-z10.1007/s00521-019-04280-z ↗
49. Potgieter, E., Stander, N.: The genetic algorithm applied to stiffness maximization of laminated plates: Review and comparison. Struct. Optim. 15, 221–229 (1998). https://doi.org/10.1007/BF0120353510.1007/BF01203535 ↗
50. Ghiasi, H., Fayazbakhsh, K., Pasini, D., Lessard, L.: Optimum stacking sequence design of composite materials part II: Variable stiffness design. Compos. Struct. 93, 1–13 (2010). https://doi.org/10.1016/j.compstruct.2010.06.00110.1016/j.compstruct.2010.06.001 ↗
51. Liu, X., Qin, J., Zhao, K., Featherston, C.A., Kennedy, D., Jing, Y., Yang, G.: Design optimization of laminated composite structures using artificial neural network and genetic algorithm. Compos. Struct. 305, 116500 (2023). https://doi.org/10.1016/j.compstruct.2022.11650010.1016/j.compstruct.2022.116500 ↗
52. Chen, Y., Zhang, J., Li, Z., Zhang, H., Chen, J., Yang, W., Yu, T., Liu, W., Li, Y.: Intelligent methods for optimization design of lightweight fiber-reinforced composite structures: a review and the-state-of-the-art. Front. Mater. 10, 1125328 (2023). https://doi.org/10.3389/fmats.2023.112532810.3389/fmats.2023.1125328 ↗
53. Bisagni, C., Lanzi, L.: Post-buckling optimisation of composite stiffened panels using neural networks. Compos. Struct. 58, 237–247 (2002). https://doi.org/10.1016/S0263-8223(02)00053-310.1016/S0263-8223(02)00053-3 ↗
54. Abouhamze, M., Shakeri, M.: Multi-objective stacking sequence optimization of laminated cylindrical panels using a genetic algorithm and neural networks. Compos. Struct. 81, 253–263 (2007). https://doi.org/10.1016/j.compstruct.2006.08.01510.1016/j.compstruct.2006.08.015 ↗
55. Marín, L., Trias, D., Badalló, P., Rus, G., Mayugo, J.A.: Optimization of composite stiffened panels under mechanical and hygrothermal loads using neural networks and genetic algorithms. Compos. Struct. 94, 3321–3326 (2012). https://doi.org/10.1016/j.compstruct.2012.04.02410.1016/j.compstruct.2012.04.024 ↗
56. Pitton, S.F., Ricci, S., Bisagni, C.: Buckling optimization of variable stiffness cylindrical shells through artificial intelligence techniques. Compos. Struct. 230, 111513 (2019). https://doi.org/10.1016/j.compstruct.2019.11151310.1016/j.compstruct.2019.111513 ↗
57. Singh, K., Kapania, R.K.: Accelerated optimization of curvilinearly stiffened panels using deep learning. Thin-Walled Struct. 161, 107418 (2021). https://doi.org/10.1016/j.tws.2020.10741810.1016/j.tws.2020.107418 ↗
58. Peng, X., Qiu, C., Li, J., Wu, H., Liu, Z., Jiang, S.: Multiple-scale uncertainty optimization design of hybrid composite structures based on neural network and genetic algorithm. Compos. Struct. 262, 113371 (2021). https://doi.org/10.1016/j.compstruct.2020.11337110.1016/j.compstruct.2020.113371 ↗
59. Bessa, M.A., Pellegrino, S.: Design of ultra-thin shell structures in the stochastic post-buckling range using bayesian machine learning and optimization. Int. J. Solids Struct. 139–140, 174–188 (2018). https://doi.org/10.1016/j.ijsolstr.2018.01.03510.1016/j.ijsolstr.2018.01.035 ↗
60. Ehsani, A., Dalir, H.: Multi-objective optimization of composite angle grid plates for maximum buckling load and minimum weight using genetic algorithms and neural networks. Compos. Struct. 229, 111450 (2019). https://doi.org/10.1016/j.compstruct.2019.11145010.1016/j.compstruct.2019.111450 ↗
61. Roy, A.K., Tsai, S.W.: Design of Thick Composite Cylinders. J. Press Vessel Technol. 110, 255–262 (1988). https://doi.org/10.1115/1.326559710.1115/1.3265597 ↗
62. Hartung, R.F.: Planar-wound filamentary pressure vessels. AIAA J. 1, 2842–2844 (1963). https://doi.org/10.2514/3.218110.2514/3.2181 ↗
63. Knoell, A.: Structural design and stress analysis program for advanced composite filament-wound axisymmetric pressure vessels (COMTANK). Comput. Aided Des. 5, 267 (1973). https://doi.org/10.1016/0010-4485(73)90309-610.1016/0010-4485(73)90309-6 ↗
64. Duan, C.H., Luo, X.P., Zhang, N.: Stress and strain analysis of Composite Gas Cylinder based on ABAQUS. AMR 557–559, 300–303 (2012). https://doi.org/10.4028/www.scientific.net/AMR.557-559.30010.4028/www.scientific.net/AMR.557-559.300 ↗
65. Fernández-Fdz, D., López-Puente, J., Zaera, R.: Prediction of the behaviour of CFRPs against high-velocity impact of solids employing an artificial neural network methodology. Compos. Part A Appl. Sci. Manufac. 39, 989–996 (2008). https://doi.org/10.1016/j.compositesa.2008.03.00210.1016/j.compositesa.2008.03.002 ↗
66. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature1453910.1038/nature14539 ↗
67. Benardos, P.G., Vosniakos, G.-C.: Optimizing feedforward artificial neural network architecture. Eng. Appl. Artif. Intell. 20, 365–382 (2007). https://doi.org/10.1016/j.engappai.2006.06.00510.1016/j.engappai.2006.06.005 ↗
68. Yuen, K.-V., Lam, H.-F.: On the complexity of artificial neural networks for smart structures monitoring. Eng. Struct. 28, 977–984 (2006). https://doi.org/10.1016/j.engstruct.2005.11.00210.1016/j.engstruct.2005.11.002 ↗
69. Kalita, K., Dey, P., Haldar, S.: Robust genetically optimized skew laminates. Proc. Institution Mech. Eng. Part C: J. Mech. Eng. Sci. 233, 146–159 (2019). https://doi.org/10.1177/095440621875694310.1177/0954406218756943 ↗