From package and design surfaces to optimization - how to apply shape optimization under geometrical constraints

Werner, Yannis; Thiele, Philip; Gopalan, Vijey Subramani Raja; Vietor, Thomas

doi:10.1016/j.procir.2021.05.118

Cited by 2 publications

(8 citation statements)

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“…Another problem is the ratio of infeasible designs by the intersecting hull surface, which reduces the number of calculated designs [18,20]. Accordingly, the sampling size of actual calculated samples shrinks by a factor of 0.2 for the given sample.…”

Section: Optimization Results For the Linear Load Case With Infeasibi...mentioning

confidence: 99%

“…The basic framework and approach are described in [18]. In addition, an extension of the method and more detailed optimizations are performed in [20]. The general framework is a Python 3-based implementation for the multi-objective structural shape and size optimization of linear and non-linear load cases for structural components.…”

Section: Ikos: the Frameworkmentioning

confidence: 99%

“…The NSGA-II optimized the parts but showed slow convergence ratios and many function evaluations. Still, the NSGA-II is a robust algorithm for optimizing a b-pillar under the given geometrical constraint [20]. Furthermore, the framework allows for the parallelization of the calculations and server support.…”

Section: Ikos: the Frameworkmentioning

confidence: 99%

“…The proof of concept for the IKOS configuration used the well-established NSGA-II algorithm, following [19]. In [20], the authors applied the approach to a more complex and automotive-relevant preliminary design scenario of a b-pillar. The optimization showed that the NSGA-II tends to optimize slow and that the use of surrogate models could help to improve the speed of convergence.…”

Section: Introductionmentioning

confidence: 99%

“…The Results section compares the performance of the NSGA-II to the implementation of the selected GOMORS framework in a comparative study [21]. The optimizations use the implemented b-pillar from [20] as a model. Furthermore, different load cases are analyzed for both algorithms: a torsional (linear), a side-impact (non-linear), and the side-impact load case under the geometrical constraint.…”

Section: Introductionmentioning

confidence: 99%

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Test and Validation of the Surrogate-Based, Multi-Objective GOMORS Algorithm against the NSGA-II Algorithm in Structural Shape Optimization

et al. 2022

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Nowadays, product development times are constantly decreasing, while the requirements for the products themselves increased significantly in the last decade. Hence, manufacturers use Computer-Aided Design (CAD) and Finite-Element (FE) Methods to develop better products in shorter times. Shape optimization offers great potential to improve many high-fidelity, numerical problems such as the crash performance of cars. Still, the proper selection of optimization algorithms provides a great potential to increase the speed of the optimization time. This article reviews the optimization performance of two different algorithms and frameworks for the structural behavior of a b-pillar. A b-pillar is the structural component between a car’s front and rear door, loaded under static and crash requirements. Furthermore, the validation of the algorithm includes a feasibility constraint. Recently, an optimization routine was implemented and validated for a Non-dominated Sorting Genetic Algorithm (NSGA-II) implementation. Different multi-objective optimization algorithms are reviewed and methodically ranked in a comparative study by given criteria. In this case, the Gap Optimized Multi-Objective Optimization using Response Surfaces (GOMORS) framework is chosen and implemented into the existing Institut für Konstruktionstechnik Optimizes Shapes (IKOS) framework. Specifically, the article compares the NSGA-II and GOMORS directly for a linear, non-linear, and feasibility optimization scenario. The results show that the GOMORS outperforms the NSGA-II vastly regarding the number of function calls and Pareto-efficient results without the feasibility constraint. The problem is reformulated to an unconstrained, three-objective optimization problem to analyze the influence of the constraint. The constrained and unconstrained approaches show equal performance for the given scenarios. Accordingly, the authors provide a clear recommendation towards the surrogate-based GOMORS for costly and multi-objective evaluations. Furthermore, the algorithm can handle the feasibility constraint properly when formulated as an objective function and as a constraint.

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Section: Optimization Results For the Linear Load Case With Infeasibi...mentioning

confidence: 99%

Section: Ikos: the Frameworkmentioning

confidence: 99%

Section: Ikos: the Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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