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Multi-Objective Restraint System Robustness and Reliability Design Optimization with Advanced Data Analytics
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
This study deals with passenger side restraint system design for frontal impact and four impact modes are considered in optimization. The objective is to minimize the Relative Risk Score (RRS), defined by the National Highway Traffic Safety Administration (NTHSA)'s New Car Assessment Program (NCAP). At the same time, the design should satisfy various injury criteria including HIC, chest deflection/acceleration, neck tension/compression, etc., which ensures the vehicle meeting or exceeding all Federal Motor Vehicle Safety Standard (FMVSS) No. 208 requirements. The design variables include airbag firing time, airbag vent size, inflator power level, retractor force level. Some of the restraint feature options (e.g., some specific features on/off) are also considered as discrete design variables. Considering the local variability of input variables such as manufacturing tolerances, the robustness and reliability of nominal designs were also taken into account in optimization process. Genetic Algorithms (GA) based optimization methods were applied because these methods can handle discrete and continuous design variables simultaneously, as well treat such highly nonlinear optimization problems in a robust manner. Frontal impact generic passenger side MADYMO models were developed to simulate the full frontal 90-degree rigid barrier scenarios at different impact speeds with a belted or an unbelted occupant. Both deterministic and robustness/reliability multi-objective optimizations were performed, and Pareto solutions were obtained. Advanced data analytics tools such as Principle Component Analysis (PCA) and Hierarchical Cluster Analysis are utilized to identify a handful representative Pareto solutions from the original large set of Pareto solutions. MCDM (Multi Criteria Decision Making) is used to rank Pareto solutions based on decision makers’ preferences and help decision makers evaluate alternatives easily. This paper focuses the practices of these tools, summarizes the pros and cons of these tools, and provides helps to engineers, especially how to analyze the results from optimization and when to apply these advanced data analytics tools.
CitationLi, G., Xue, Z., Pline, K., and Gao, Z., "Multi-Objective Restraint System Robustness and Reliability Design Optimization with Advanced Data Analytics," SAE Technical Paper 2020-01-0743, 2020, https://doi.org/10.4271/2020-01-0743.
Data Sets - Support Documents
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- Li, G., Xue, Z., Chuang, C.-H., and Pline, K. , “Study of Optimization Strategy for Vehicle Restraint System Design,” SAE Technical Paper 2019-01-1072, 2019, https://doi.org/10.4271/2019-01-1072.
- Xue, Z., Marchi, M., Parashar, S., and Li, G. , “Comparing Uncertainty Quantification with Polynomial Chaos and Metamodel-Based Strategies for Computationally Expensive CAE Simulations and Optimization Applications,” SAE Technical Paper 2015-01-0437, 2015, https://doi.org/10.4271/2015-01-0437.
- Xue, Z., Parashar, S., Li, G., and Fu, Y. , “Optimization Strategies to Explore Multiple Optimal Solutions and Its Application to Restraint System Design,” SAE Int. J. Passeng. Cars - Mech. Syst. 5(1), 2012, https://doi.org/10.4271/2012-01-0578.
- Craig, K.J., Stander, N., Dooge, D.A., and Varadappa, S. , “Automotive Crashworthiness Design Using Response Surface-Based Variable Screening and Optimization,” Engineering Computations: International Journal for Computer-Aided Engineering and Software 22(1):38-61, 2005.
- modeFRONTIER® is a product of ESTECO, www.esteco.com.
- Weistroffer, H.R., Smith, C.H., and Narula, S.C. , “Multiple Criteria Decision Support Software,” . In: Figueira, J.,Greco, S., andEhrgott, M., editors. Multiple Criteria Decision Analysis: State of the Art Surveys Series. (New York, Springer, 2005).
- Xu, H., Chuang, C., and Yang, R. , “A Data Mining-Based Strategy for Direct Multidisciplinary Optimization,” SAE Int. J. Mater. Manf. 8(2):357-363, 2015, https://doi.org/10.4271/2015-01-0479.
- Liu, Z., Zhu, P., Chen, W., and Yang, R.J. , “Improved Particle Swarm Optimization Algorithm Using Design of Experiment and Data Mining Techniques,” Structural and Multidisciplinary Optimization 52(4):813-826, 2015.
- MacQueen, J.B. , “Some Methods for classification and Analysis of Multivariate Observations,” in Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 1967, 1, University of California Press, 281-297.
- Rokach, L. and Oded, M. , “Clustering Methods,” Data Mining and Knowledge Discovery Handbook (Springer US, 2005), 321-352.
- Poles, S., Fu, Y., and Rigoni, E. , “The Effect of Initial Population Sampling on the Convergence of Multi-Objective Genetic Algorithms,” in MOPGP'06: 7th Int. Conf. on Multi-Objective Programming and Goal Programming, 2006, doi:10.1007/978-3-540-85646-7_12.
- Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. , “A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II,” IEEE Transaction on Evolutionary Computation 6(2):181-197, 2002, doi:10.1109/4235.996017.
- Costanzo, S., Xue, Z., Engel, M., and Chuang, C.H. , “Multi-Strategy Intelligent Optimization Algorithm for Computationally Expensive CAE Simulations,” in NAFEMS World Congress/SPDM Conference, 2015.
- Gu, C. , Smoothing Spline ANOVA Models (New York: Springer-Verlag, 2002).
- Shi, L., Fu, Y., Yang, R.J., Wang, B.P., and Zhu, P. , “Selection of Initial Designs for Multi-Objective Optimization Using Classification and Regression Tree,” Structural and Multidisciplinary Optimization 48(6):1057-1073, 2013.