|
Abstract:
|
Polymer composite materials are widely used in areas such as aerospace and alternative energy industries, due to their lightweight and comparable levels of strength and endurance. To ensure that the material can last long enough in the field, accelerated cyclic fatigue tests are commonly used to collect data and then make predictions for the field performance. Thus, a good testing strategy is desirable for evaluating the property of polymer composites. While there has been a lot of development in optimum test planning, most of the methods assume that the true parameter values are known. However, in reality, the true model parameters may depart from the planning values. In this study, we propose a sequential strategy for test planning, and use a Bayesian framework for sequential model updating. We also use extensive simulations to evaluate the properties of the proposed sequential test planning strategy and compare the proposed method to traditional optimum designs. Our results show that the proposed strategy is more robust and efficient, as compared to the optimum designs when true values of parameters are unknown.
|
