Data mining application on crash simulation data of occupant restraint system

This article presents an application of data mining method on finite element data and crashworthiness result data of an occupant restraint system. According to the characteristics of the CAE (Computer-Aided Engineering) data, a framework for data preparation is developed based on object-oriented programming concepts. Training sets are built from data recorded in 98 crash simulations that adhere to FMVSS208, the America occupant crash protection testing standard. Relationship between design parameters and system effectiveness is implied in these data sets. Decision tree using C4.5 algorithm and attribute selection method based on attribute’s estimated importance are introduced to perform data mining on the building of training sets. The result yielded by data mining endows us with a deeper insight into the interrelations between the key design parameters and the performance of the occupant restraint system in crash simulations. Finally, the learned rules are tested on the real crash simulation data sets. The result of the testing shows that these rules are proper, and can been used as a guidance for the design of the occupant restraint system.