This is an open access article distributed under the CC BY 4.0
Volume 21 article 1068 pages: 241-252
This paper aims to establish a metamodel for predicting the mechanical behavior of bolted structures with elastic parts, regardless the changes in input parameters from a set of simulation data. First, we collect information from a parametric analysis based on numerical finite element simulation tests. Then, the metamodel is built using the radial spline basis function method. Following that, an iterative fitting process based on the metamodel-simulation coupling is used to improve the model’s fidelity. Finally, the metamodel is validated by comparing and analysing the error rate between the metamodel and the simulation in order to reduce the computation time towards 2 seconds.
1. Do Amaral, J. V. S., Montevechi, J. A. B., de Carvalho Miranda, R., & de Sousa Junior, W. T. (2022). Metamodel-based simulation optimization: A systematic literature review. Simulation Modelling Practice and Theory, vol.114, p.102403.
2. Haiek, H., El Ansari, Y., Amrani Ben Said, N., Sarsri, D. (2020). A Stochastic Model of Stress Evolution in a Bolted Structure in the Presence of a Joint Elastic Piece: Modeling and Parameter Inference. Advances in Materials Science and Engineering, DOI ://doi.org/10.1155/2020/9601212
3. Zhang, Z., Xiao, Y., Xie, Y., & Su, Z. (2019). Effects of contact between rough surfaces on the dynamic responses of bolted composite joints: multiscale modeling and numerical simulation. Composite Structures, vol. 211, p.13-23.
4. Yunus, M. A., Nazri, S., Rani, M. N. A., Tormodi, A., & Kasolang, S. (2017). Response surface reconciliation method of bolted joints structure. In MATEC Web of Conferences. EDP Sciences. vol. 90, p. 01013.
5. Mathern, A., Penadés-Plà, V., Armesto Barros, J., & Yepes, V. (2022). Practical metamodel-assisted multi-objective design optimization for improved sustainability and buildability of wind turbine foundations. Structural and Multidisciplinary Optimization, vol. 65, no. 2, p. 1-16.
6. Booth, D.N., Cohar, C.P., Inal, K. (2021). Multi-objective optimization of a multi-cellular aluminum extruded crush rail sub- jected to dynamic axial and oblique impact loading conditions. Thin-Walled Structures, vol. 166, pp.108021.
7. Zhang, J., Xiao, M., Li, P., Gao, L. (2022). Quantile-based topology optimization under uncertainty using Kriging metamo- del. Computer Methods in Applied Mechanics and Engineering, vol. 393, p. 114690.
8. Zhang, J., Xiao, M., Gao, L., Fu, J. (2018) A novel projection outline based active learning method and its combination with Kriging metamodel for hybrid reliability analysis with random and interval variables. Computer Methods in Applied Mechanics and Engineering, vol. 198, p. 32-52.
9. Eremeeva, P., De Cockb, A., Devriendt, H., Melckenbeeck, I., Desmet W. (2022). Single and multi-objective optimization of a gearbox considering dynamic performance and assemblability. Procedia CIRP, vol. 106, p. 76-83.
10. Bi, Z., & Wang, X. (2020). Computer aided design and manufacturing. John Wiley & Sons.
11. De Sousa JuniorJosé, W.T., Barra Montevechi, J.A., De Carvalho Mirand, R., Moura de Oliveira, M., Campos, A.T. (2020). Shop floor simulation optimization using machine learning to improve parallel metaheuristics. Expert Systems with Ap- plications, vol. 150. p. 113272.
12. Nguye, P.T., Di Ruscio, D.,Alfonso Pierantonio, A., Di Rocco, J., Iovino, L. (2021). Convolutional neural networks for enhanced classification mechanisms of metamodels. The Journal of Systems Software, vol. 172, p. 110860.
13. Song, W., Han, K., Wang, Y., Friesz, T., & Del Castillo, E. (2017). Statistical metamodeling of dynamic network loading. Transportation research procedia, vol. 23, p. 263-282.
14. Roman, N.D., Bre, F., Fachinotti, V.D., Lamberts, R. (2020). Application and characterization of metamodels based on artificial neural networks for building performance simulation: A systematic review. Energy & Buildings, vol. 217, p. 109972.
15. Sarra, S.A., Bai, Y. (2018). A rational radial basis function method for accurately resolving discontinuities and steep gradients. Applied Numerical Mathematics, vol.130, p. 131-142.
16. Jensen, W. A. (2017). Response surface methodology: process and product optimization using designed experiments. Journal of Quality Technology, vol. 49, no. 2, p. 186.