Multi-objective and multi-case reliability-based design optimization for tailor rolled blank (TRB) structures

Light weight and crashworthiness signify two main challenges facing in vehicle industry, which often conflict with each other. In order to achieve light weight while improving crashworthiness, tailor rolled blank (TRB) has become one of the most potential lightweight technologies. To maximize the ch...

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Bibliographic Details
Published in:Structural and multidisciplinary optimization 2017-05, Vol.55 (5), p.1899-1916
Main Authors: Sun, Guangyong, Zhang, Huile, Fang, Jianguang, Li, Guangyao, Li, Qing
Format: Article
Language:English
Subjects:
Classification
Computational Mathematics and Numerical Analysis
Computer simulation
Crashworthiness
Design optimization
Engineering
Engineering Design
Fuel consumption
Genetic algorithms
Impact strength
Industrial Application
Metamodels
Monte Carlo simulation
Multiple objective analysis
Parameter uncertainty
Radial basis function
Sorting algorithms
Theoretical and Applied Mechanics
Weight reduction
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Summary:Light weight and crashworthiness signify two main challenges facing in vehicle industry, which often conflict with each other. In order to achieve light weight while improving crashworthiness, tailor rolled blank (TRB) has become one of the most potential lightweight technologies. To maximize the characteristics of TRB structures, structural optimization has been adopted extensively. Conventional optimization studies have mainly focused on a single loading case (SLC). In practice, however, engineering structures are often subjected to multiple loading cases (MLC), implying that the optimal design under a certain condition may no longer be an optimum under other loading cases. Furthermore, traditional deterministic optimization could become less meaningful or even unacceptable when uncertainties of design variables and noises of system parameters are present. To address these issues, a multi-objective and multi-case reliability-based design optimization (MOMCRBDO) was developed in this study to optimize the TRB hat-shaped structure. The radial basis function (RBF) metamodel was adopted to approximate the responses of objectives and constraints, the non-dominated sorting genetic algorithm II (NSGA-II), coupled with Monte Carlo Simulation (MCS), was employed to seek optimal reliability solutions. The optimal results show that the proposed method is not only capable of improving the reliability of Pareto solutions, but also enhancing the robustness under MLC.
ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-016-1592-1