Abstract
Work presented in this article concerns an industrial example of a mass optimization problem, performed on automotive suspension components. Due to the fact, that the considered element, i.e. spring seat is a part of the vehicle suspension module, analyses are performed on the shock absorber assembly level with the mutual interaction of each body. To solve constrained optimization task, a combination of genetic and gradient-based algorithms are used, involving nonlinear static strength problem, represented by finite element method analysis. Parametric representation of considered component geometry is used, incorporating manufacturing restrictions and design assembly requirements. The final, optimized form of the spring seat is then compared with its initial shape, i.e. “working space”, especially under constraints fulfillment, which are specified by the customer. Performance comparison of used methods (or combination of such) is the aim of this paper, focusing on practical aspects of work, i.e. final mass decrease, number of function calls, or design space exploration capabilities. The optimization method presented hereby is universal and can be adapted to many fields of industry, wherever efficient mass reduction must be achieved incorporating the feasibility of the component. The hybrid optimization approach presented in the paper allows effective mass reduction and works well for multimodal objective functions typically found in industrial applications.
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Andersen, S. B. and Santos, I. F. (2012). Evolution strategies and multi-objective optimization of permanent magnet motor. Applied Soft Computing 12,2, 778–792.
Beyer, H. G. and Schwefel, H. P. (2002). Evolution strategies-A comprehensive introduction. Natural Computing 1, 1, 3–52.
Burczynski, T. and Orantek, P. (1999). Coupling of genetic and gradient algorithms. Potok Zloty: Proc. Conf. Evolutionary Algorithms and Global Optimization, 112–114.
Burczynski, T., Kus, W., Beluch, W., Dlugosz, A., Poteralski, A. and Szczepanik, M. (2020). Intelligent Computing in Optimal Design. Springer, Cham, Switzerland.
Charbuillet, C., Gas, B., Chetouani, M. and Zarader, J. L. (2009). Optimizing feature complementarity by evolution strategy: Application to automatic speaker verification. Speech Communication 51, 9, 724–731.
Grasso, F. (2011). Usage of hybrid optimization scheme for wind turbine thick airfoils design. ERCOFTAC ECCOMAS, Eurogen 2011, Capua, Italy.
He, H., Yi, L. and Peng, J. (2017). Combinatorial optimization algorithm of MIGA and NLPQL for a plug-in hybrid electric bus parameters optimization. Energy Procedia, 105, 2460–2465.
Hu, X., Chen, X., Zhao, Y. and Yao, W. (2014). Optimization design of satellite separation systems based on multi-island genetic algorithm. Advances in Space Research 53, 5, 870–876.
Liu, M. C., Zhang, C. N. and Wang, Z. F. (2012). Research on the influence of unsprung mass on vehicle handling stability. Advanced Materials Research, 562, 816–820.
Luo, Y., Lu, T. and Du, X. (2018). Novel optimization design strategy for solar power tower plants. Energy Conversion and Management, 177, 682–692.
Montastruc, L., Azzaro-Pantel, C., Pibouleau, L. and Domenech, S. (2004). Use of genetic algorithms and gradient based optimization techniques for calcium phosphate precipitation. Chemical Engineering and Processing: Process Intensification 43, 10, 1289–1298.
Prebeg, P., Zanic, V. and Vazic, B. (2014). Application of a surrogate modeling to the ship structural design. Ocean Engineering, 84, 259–272.
Schittkowski, K. (1986). NLPQL: A FORTRAN subroutine solving constrained nonlinear programming problems. Annals of Operations Research 5, 2, 485–500.
Schittkowski, K., Zillober, C. and Zotemantel, R. (1994). Numerical comparison of nonlinear programming algorithms for structural optimization. Structural Optimization 7, 1–2, 1–19.
Shin, M. K., Lee, H. A., Lee, J. J., Song, K. N. and Park, G. J. (2008). Optimization of a nuclear fuel spacer grid spring using homology constraints. Nuclear Engineering and Design 238, 10, 2624–2634.
Vanderplaats, G. N. (1984). An efficient feasible directions algorithm for design synthesis. AIAA J. 22, 11, 1633–1640.
Vanderplaats, G. N. (1987). ADS, A FORTRAN Program for Automated Design Synthesis. Engineering Design Optimization Inc.
Wahl, P. E., Løvseth, S. W. and Mølnvik, M. J. (2013). Optimization of a simple LNG process using sequential quadratic programming. Computers & Chemical Engineering, 56, 27–36.
Whitley, D., Rana, S. and Heckendorn, R. B. (1999). The island model genetic algorithm: On separability, population size and convergence. J. Computing and Information Technology 7, 1, 33–47.
Zhao, D. J., Wang, Y. K., Cao, W. W. and Zhou, P. (2015). Optimization of suction control on an airfoil using multi-island genetic algorithm. Procedia Engineering, 99, 696–702.
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The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: the research was co-financed under the grant no DWD/3/7/2019 supported by the Ministry of Science and Higher Education in Poland and research subsidy of the Mechanical Engineering Faculty, Silesian University of Technology.
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Sebastjan, P., Kuś, W. Hybrid Shape Optimization of Automotive Spring Seat. Int.J Automot. Technol. 23, 957–965 (2022). https://doi.org/10.1007/s12239-022-0083-1
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DOI: https://doi.org/10.1007/s12239-022-0083-1