Multi-objective Restraint System Robust and Reliability Design Optimization with Advanced Data Analytics 2020-01-0743
Vehicle restraint system design optimization is important for occupant protection and achieving high score in NCAP rating of five-Star. The target is to minimize the Relative Risk Score (RRS), defined by the National Highway Traffic Safety Administration (NTHSA)'s New Car Assessment Program (NCAP).
The design input includes restraint feature options (e.g., some specific features on/off) as discrete design variables, as well as continuous restraint design variables, such as airbag firing time, airbag vent size, inflator power level, etc. The optimization problem is constrained by injury criteria involve 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.
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
In this study, frontal impact modes were used as example case. 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 Cluster Analysis (K-mean and Hierarchical), are utilized to identify a handful representative Pareto solutions from the original large set of Pareto solutions. Self-Organizing Maps (SOM) are used to reveal local correlations among all attributes include design variables and responses. This paper focuses the practices of these tools, summarizes the pros and cons of these tools, and provides guidelines to engineers, especially how to analyze the results from optimization and when to apply these advanced data analytics tools.
Guosong Li, Zhendan Xue, Kevin Pline, Zhenyan Gao
Ford Motor Company, ESTECO North America