Advancement of Vehicle Occupant Restraint System Design by Integration of Artificial Intelligence Technologies

In order to improve the design method of vehicle occupant restraint systems, it is necessary to reduce the computational load of simulations, to improve the global search capability, and to examine and integrate analytical methods to understand the complex interaction between design variables and objective functions. Therefore, in this study, we integrated the following three artificial intelligence technologies and applied them to the design of a vehicle occupant restraint system: (1) construction of a highly accurate approximate model by machine learning, (2) improvement of global search capability by evolutionary multi-objective optimization and (3) visualization and knowledge acquisition of multidimensional information using multivariate analysis methods. First, we obtained the minimum number of actual calculation samples using a crash analysis model with the design of experiments, and then used these samples to construct a highly accurate approximate model using machine learning. Next, we used the approximate model to perform a global search in the design space by evolutionary multi-objective optimization to obtain a pareto solution set that takes into account the trade-off relationship between the objective functions. finally, multivariate analysis using cluster analysis and self-organizing maps was performed on the pareto solution set. As a result, a fast global search was realized by substituting the evaluation calculation of evolutionary multi-objective optimization with a highly accurate approximate model. The pareto solution set obtained therein was then partitioned into clusters by cluster analysis, and the partitioned clusters were analysed by self-organizing maps, which provided perceptual information on the factors governing the trade-offs between the objective functions and the interactions between the design variables, and were useful for design engineers’ insights.