473
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A method for designing filament-wound composite frame structures using a data-driven evolutionary optimisation algorithm EvoDN2

ORCID Icon, ORCID Icon, ORCID Icon &
Article: 2272975 | Received 10 Jul 2023, Accepted 09 Oct 2023, Published online: 28 Oct 2023

References

  • F.C. Shen, A filament-wound structure technology overview. Mater. Chem. Phys. 42(2) (1995), pp. 96–100.
  • M. Dvořák, T. Ponížil, V. Kulíšek, N. Schmidová, K. Doubrava, B. Kropík, and M. Růžička, Experimental development of composite bicycle frame. Appl. Sci. 12(16) (2022), p. 8377. doi:10.3390/app12168377.
  • S. David Müzel, E.P. Bonhin, N.M. Guimarães, and E.S. Guidi, Application of the finite element method in the analysis of composite materials: a review. Polymers 12(4) (2020), pp. 818.
  • A. Malá, Z. Padovec, T. Mareš, and N. Chakraborti, Shallow and deep evolutionary neural networks applications in solid mechanics, in Advanced Machine Learning with Evolutionary and Metaheuristic Techniques, J. Valadi, K.P. Singh, M. Ojha, and P. Siarry, ed., Springer, Springer Nature Singapore Pvt Ltd.
  • K. Miettinen, Nonlinear Multiobjective Optimization, Kluwer Academic Publishers, Boston, 1998.
  • D. Vondráček, Z. Padovec, T. Mareš, and N. Chakraborti, Optimization of dome shape for filament wound pressure vessels using data-driven evolutionary algorithms. Mater. Manuf. Processes (2023), pp. 1–12. doi:10.1080/10426914.2023.2187823.
  • D. Vondráček, Z. Padovec, T. Mareš, and N. Chakraborti, Analysis and optimization of junction between cylindrical part and end dome of filament wound pressure vessels using data driven evolutionary algorithms. Proc. Inst. Mech. Eng., Part C (2023). doi:10.1177/09544062231191319.
  • S. Roy, and N. Chakraborti, Novel strategies for data-driven evolutionary optimization, in Computational Sciences and Artificial Intelligence in Industry: New Digital Technologies for Solving Future Societal and Economical Challenges, Tero Turovien, Jacques Periaux, Pekka Neittaanmäki, ed., Springer International Publishing, Cham, 2022. pp. 11–25.
  • R.M. Jones, Mechanics of Composite Materials: Second ed, Taylor & Francis Ltd, London, 1999.
  • S.S. Rao, The Finite Element Method in Engineering, Elsevier Science & Technology, Burlington, 2004.
  • R. Cheng, Y. Jin, M. Olhofer, and B. Sendhoff, A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 20(5) (2016), pp. 773–791.
  • S. Roy, B. Saini, D. Chakrabarti, and N. Chakraborti, Mechanical properties of micro-alloyed steels studied using a evolutionary deep neural network. Mater. Manuf. Processes 35(6) (2020), pp. 611–624.
  • N. Chakraborti, Data-Driven Evolutionary Modeling in Materials Technology, CRC Press, Boca Raton, 2023.
  • P. David, T. Mareš, and N. Chakraborti, Evolutionary multi-objective optimization of truss topology for additively manufactured components. Mater. Manuf. Processes (2023), pp. 1–10. doi:10.1080/10426914.2023.2196325.
  • X. Li, P. Yang, Z. Chen, and L. Miao, The modular design and analysis of open CNC system machine. MATEC Web Conf. 220 (2018), p. 08006.
  • Z. Liao, C. Qiu, J. Yang, J. Yang, and L. Yang, Accelerating the layup sequences design of composite laminates via theory-guided machine learning models. Polymers 14(15) (2022), pp. 3229.
  • P. Bastl, M. Valášek, and N. Chakraborti, Evolutionary algorithms in robot calibration. Mater. Manuf. Processes (2023), pp. 1–20. doi:10.1080/10426914.2023.2238368.
  • Z. Duan, Y. Liu, B. Xu, and J. Yan, Structural topology design optimization of fiber-reinforced composite frames with fundamental frequency constraints. J. Struct. Eng. 148(4) (2022), p. 04022027.