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Accurate prediction of the responses from the anthropomorphic test devices (ATDs) in vehicle crash tests is critical to achieving better vehicle occupant performances. In recent years, automakers have used finite element (FE) models of the ATDs in computer simulations to obtain early assessments of occupant safety, and to aid in the development of occupant restraint systems. However, vehicle crash test results have variation, sometimes significant. This presents a challenge to assessing the accuracy of the ATD FE models, let alone improving them. To resolve this issue, it is important to understand the test variation and carefully select the target data for model improvement. This paper presents the work carried out by General Motors and Humanetics Innovative Solutions (formerly FTSS) in a joint project, aimed at improving the FE model of the Hybrid III-50 ATD (HIII-50) v5.1.

As part of the Federal Motor Vehicle Safety Standards, requirements for roof strength need to be met for all vehicles. On the other hand, automobile manufactures need to minimize vehicle mass for fuel economy and other objectives. It is important, therefore, for manufacturers to have a good understanding of the sources of variation in measured roof strength. An accurate estimation of such variation is important to achieving these objectives. This paper presents a method of using CAE simulation and vehicle tests to effectively estimate the range of variability in the roof crush tests. A number of vehicle and test variables which could potentially affect the measured roof strength were chosen, and their sensitivity was evaluated through CAE simulation. This knowledge of the sensitivity was then used to design a small number of vehicle tests, producing an estimation of the variation range in roof strength.

This paper suggests a scientific approach to designing conservative tests based on computer simulation of the influence of the sources of variations. The idea is to design the conservative test so that, even in the presence of variation, there is a high probability that a random test will have a better result than the conservative test. Therefore, if the conservative test meets the requirement, one has a scientific reason to believe that any random test would have a high probability of meeting it. This new approach is illustrated for FMVSS301 80 kph 70% rear offset deformable barrier impact.

In performing stochastic simulations using computer models, the method of sampling is important. It affects the quality and the convergence speed of the results. This paper discusses one special case: sampling of spot-weld locations from potentially thousands of spot welds on a vehicle body. This study is prompted by the need of evaluating the effect of missed spot welds on the structural integrity, identifying critical welds, and optimizing weld locations. A balanced random sampling algorithm based on the concept of Latin-Hypercube sampling is developed for this application. We also present a case study in which the efficiency of three different sampling methods is compared using a car joint stiffness example. The new method, called the Balanced Latin-Hypercube Sampling (BLHS), has shown significantly faster convergence over the other two.

Robust optimization usually requires numerous functional evaluations, which is not feasible when the functional evaluation is time-consuming. Examples in automobile industry include crash worthiness/safety and fatigue life simulations. In practice, a response surface model (RSM) is often used as a surrogate to the CAE model, so that robust optimization can be carried out. However, if the error in the RSM is significant, the solution based on the RSM can be invalid. This paper proposes a method of finding multiple candidate solutions, all of which have similar predicted performances. This approach is effective in finding the close-to-optimum solutions when the model has error, and providing design alternatives. Examples are provided to illustrate the method.

Auto components with large manufacturing variation may cause vehicle quality problems after they are assembled. The impact of this variation depends on the assembly process used. If the assembly process is sensitive to the component variation, the impact may be more significant. In this case, an assembly process with lower sensitivity to component variation will solve the problem. This paper presents an example where the component variation largely impacted the quality of the car, and a more robust assembly process solved the problem.

When using a math model to estimate the failure rate of a product, or the mean and standard deviation of performance characteristics of the product, one important issue is the accuracy of the estimates. All math models have error. This error will be transmitted to the error in the estimates of failure rate, mean, and standard deviation. This paper presents a method to calculate the bounds on the transmitted error, which can then be used to 1) obtain confidence bounds on estimates of mean, standard deviation, and failure rate; and 2) establish accuracy requirements on math models.