Abstract
The subject of this article is the algorithm for optimization of symmetrical adhesive lapped joint. The goal of the paper is development of the method for optimization problem solving, which allows to unite high rate of calculations with stability of obtained results. This goal can be reached by means of two algorithms of optimization – genetic algorithms implemented on the original stage and swarm of particles algorithm – on the last stage of optimization. The problem of optimization is in finding optimal shape of doublers, i.e. doubler length and function of thickness variation along joint length. To describe stress-strain state of a joint modified Holland-Reissner model is used. To solve direct problem of structural stress state estimation the finite elements method is used. For optimization problem solving combination of multi-population model of genetic algorithm and swarm of particles algorithm are used. Introduction of individuals from other populations to the considered one allows to escape of homogenization of genotypes in separate population and premature breakage of optimization process. To describe doubler shape thickness variation function development of Fourier series are used. Above-mentioned implemented methods allow to create algorithm for topologic optimization which unites advantages of both methods and find solution of considered problem quite quickly. Duration of algorithm realization on Python language requires several minutes only to find optimal parameters.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dveirin, O.Z., Andreev, O.V., Kondrat’ev, A.V., Haidachuk, V.Y.: Stressed state in the vicinity of a hole in mechanical joint of composite parts. Int. Appl. Mechan. 57(2), 234–247 (2021). https://doi.org/10.1007/s10778-021-01076-4
da Silva, L.F.M., das Neves, P.J.C., Adams, R.D., Spelt J.K.: Analytical models of adhesively bonded joints. Part I: Literature survey. Int. J. Adhes. Adhes. 29(3), 319–330 (2009). https://doi.org/10.1016/j.ijadhadh.2008.0005
Amidi, S., Wang, J.: An analytical model for interfacial stresses in double-lap bonded joints. J. Adhes. 95(11), 1031–1055 (2019). https://doi.org/10.1080/00218464.2018.1464917
Ejaz, H., Mubashar, A., Ashcroft, I.A., Uddin, E., Khan, M.: Topology optimisation of adhesive joints using non-parametric methods. Int. J. Adhes. Adhes. 81, 1 (2018). https://doi.org/10.1016/j.ijadhadh.2017.11.003
Arhore, E.G., Yasaee, M., Dayyani, I.: Comparison of GA and topology optimization of adherend for adhesively bonded metal composite joints. Int. J. Solids Struct. 226–227, 111078 (2021). https://doi.org/10.1016/j.ijsolstr.2021.111078
Kaye, R., Heller, M.: Through-thickness shape optimisation of typical double lap-joints including effects of differential thermal contraction during curing. Int. J. Adhes. Adhes. 25(3), 227–238 (2005). https://doi.org/10.1016/j.ijadhadh.2004.07.006
Groth, H.L., Nordlund, P.: Shape optimization of bonded joints. Int. J. Adhes. Adhes. 11(4), 204–212 (1991). https://doi.org/10.1016/0143-7496(91)90002-y
Khadiri, I.E., Zemzami, M., Hmina, N., Lagache, M., Belhouideg, S.: Topology optimization of structures obtained by additive manufacturing: case of 3D beam. In: 2021 7th International Conference on Optimization and Applications (ICOA), 235307775 (2021). https://doi.org/10.1109/icoa51614.2021.9442628
Kurennov, S., Barakhov, K., Polyakov, O., Taranenko, I.: Application of genetic algorithm for double-lap adhesive joint design. Archiv. Mech. Eng. 70(1), 27–42 (2023). https://doi.org/10.24425/ame.2022.144074
Kurennov, S., Barakhov, K., Vambol, O.: Topological optimization of a symmetrical adhesive joint. Island model of genetic algorithm. Radioelectron. Comput. Syst. 3, 67–83 (2022). https://doi.org/10.32620/reks.2022.3.05
Kurennov, S., Barakhov, K., Vambol, O.: Topological optimization BI-adhesive double lap adhesive joint. One-dimension model. Int. J. Adhes. Adhes. 126, 103474 (2023). https://doi.org/10.1016/j.ijadhadh.2023.103474
Wei, N., Ye, H., Wang, W., Li, J., Tian, F., Sui, Y.: Topology optimization for hybrid lattice compliant mechanisms with multiple microstructures. Materials 15(20), 7321 (2022). https://doi.org/10.3390/ma15207321
Zhu, B., et al.: Design of compliant mechanisms using continuum topology optimization: a review. Mech. Mach. Theory 143, 103622 (2020). https://doi.org/10.1016/j.mechmachtheory.2019.103622
Robinson, J., Sinton, S., Rahmat-Samii, Y.: Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna. In: IEEE Antennas and Propagation Society International Symposium (IEEE Cat. No.02CH37313), San Antonio, vol. 1, pp. 314–317 (2002). https://doi.org/10.1109/APS.2002.1016311
Li, J., Gonsalves, T.: A hybrid approach for metaheuristic algorithms using island model. In: Arai, K. (eds.) Proceedings of the Future Technologies Conference (FTC) 2021, 3. FTC 2021. LNNS, vol. 360. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-89912-7_24
Kurennov, S., Barakhov, K., Taranenko, I., Stepanenko, V.: A genetic algorithm of optimal design of beam at restricted sagging. Radioelectron. Comput. Syst. 1(101), 83–91 (2022). https://doi.org/10.32620/reks.2022.1.06
Zhang, W.-J., **e, X.-F.: DEPSO: hybrid particle swarm with differential evolution operator. In: Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme – System Security and Assurance (Cat. No. 03CH37483) (SMC 2003), Washington, vol. 4, pp. 3816–3821 (2003). https://doi.org/10.1109/ICSMC.2003.1244483
Shami, T.M., El-Saleh, A.A., Alswaitti, M., Al-Tashi, Q., Summakieh, M.A., Mirjalili, S.: Particle swarm optimization: a comprehensive survey. IEEE Access 10, 10031–10061 (2022). https://doi.org/10.1109/ACCESS.2022.3142859
Rana, S., Sarwar, Md., Anwar Shahzad Siddiqui, P.: Particle swarm optimization: an overview, advancements and hybridization. In: Optimization Techniques in Engineering: Advances and Applications, pp. 95–113 (2023). https://doi.org/10.1002/9781119906391.ch6
Lee, H., Seon, S., Park, S., Walallawita, R., Lee, K.: Effect of the geometric shapes of repair patches on bonding strength. J. Adhes. 97(3), 207–224 (2019). https://doi.org/10.1080/00218464.2019.1649660
Kupski, J., Teixeira de Freitas, S.: Design of adhesively bonded lap joints with laminated CFRP adherends: review, challenges and new opportunities for aerospace structures. Compos. Struct. 268, 113923 (2021). https://doi.org/10.1016/j.compstruct.2021.113923
Kurennov, S.S., Polyakov, ОG., Barakhov, K.P.: Two-dimensional stressed state of an adhesive joint. Nonclassical problem. J. Math. Sci. 254, 156–163 (2021). https://doi.org/10.1007/s10958-021-05295-5
Koshevoy, N., Ilina, I., Tokariev, V., Malkova, A., Muratov, V.: Implementation of the gravity search method for optimization by cost expenses of plans for multifactorial experiments. Radioelectron. Comput. Syst. 1(105), 23–32 (2023). https://doi.org/10.32620/reks.2023.1.02
Bodyanskiy, Y., Shafronenko, A., Pliss, I.: Clusterization of vector and matrix data arrays using the combined evolutionary method of fish schools. Syst. Res. Inf. Technol. 4, 79–87 (2022). https://doi.org/10.20535/SRIT.2308-8893.2022.4.07
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kurennov, S., Barakhov, K., Taranenko, I., Poliakov, O.G., Barakhova, H., Vernadska, K. (2024). Hybrid Algorithm of Adhesive Joint Shape Optimization. In: Nechyporuk, M., Pavlikov, V., Krytskyi, D. (eds) Integrated Computer Technologies in Mechanical Engineering - 2023. ICTM 2023. Lecture Notes in Networks and Systems, vol 1008. Springer, Cham. https://doi.org/10.1007/978-3-031-61415-6_24
Download citation
DOI: https://doi.org/10.1007/978-3-031-61415-6_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-61414-9
Online ISBN: 978-3-031-61415-6
eBook Packages: EngineeringEngineering (R0)