Multi-Objective Restraint System Robustness and Reliability Design Optimization with Advanced Data Analytics 2020-01-0743

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.