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The overstress probe fatigue testing method, although codified to characterize fatigue strength, is poorly understood. While it yields data confirming whether minimum fatigue strength may be met, it does not directly reveal the mean fatigue strength. Procedures for conducting the test are somewhat arbitrary and rely on fitting a 3-parameter Weibull model. In this paper, a Monte Carlo procedure is developed to simulate the overstress probe test. The effect of various parameters used in the test is also discussed. A comparison is made between Weibull and Gaussian models. Suggestions for conducting the overstress probe test are provided.

Early in the engineering design cycle, it is difficult to quantify product reliability due to insufficient data or information to model uncertainties. Probability theory can not be therefore, used. Design decisions are usually, based on fuzzy information which is imprecise and incomplete. Recently, evidence theory has been proposed to handle uncertainty with limited information. In this paper, a computationally efficient design optimization method is proposed based on evidence theory, which can handle a mixture of epistemic and random uncertainties. It quickly identifies the vicinity of the optimal point and the active constraints by moving a hyper-ellipse in the original design space, using a reliability-based design optimization (RBDO) algorithm. Subsequently, a derivative-free optimizer calculates the evidence-based optimum, starting from the close-by RBDO optimum, considering only the identified active constraints.

The staircase fatigue test method is a well-established, but poorly understood probe for determining fatigue strength mean and standard deviation. The sensitivity of results to underlying distributions was studied using Monte Carlo simulation by repeatedly sampling known distributions of hypothetical fatigue strength data with the staircase test method. In this paper, the effects of the underlying distribution on staircase test results are presented with emphasis on original normal, lognormal, Weibull and bimodal data. The results indicate that the mean fatigue strength determined by the staircase testing protocol is largely unaffected by the underlying distribution, but the standard deviation is not. Suggestions for conducting staircase tests are provided.

When the finite element model of a complex structure is partitioned into substructures in order to enable component mode synthesis, the reduced order model obtained from the Craig-Bampton method often features a large number of interface degrees of freedom (DOF). The authors have recently developed a method to reduce the interface DOF by using a set of so-called characteristic constraint (CC) modes. The resultant, highly compact CC-mode-based reduced order model provides a good platform to calculate the power flow between substructures. In this paper, the CC-mode method is applied to the finite element model of a vehicle structure with about 1.5 million DOF. A convergence study is conducted to find optimal mode selection criteria, and a 2124 DOF reduced order model is obtained for the 0-200 Hz range by using the CC-mode method.