Since the formal introduction of computer experiments back in 1989, substantial work has been done to make these experiments as efficient and effective as possible. As a consequence, more and more industrial studies a...Since the formal introduction of computer experiments back in 1989, substantial work has been done to make these experiments as efficient and effective as possible. As a consequence, more and more industrial studies are performed. In this direction, sequential strategies have been introduced with the aim of reducing the experimental effort while keeping the required accuracy of the meta-model. The strategies consist in building a fairly accurate meta-model based on a low number of experimental points, and then adding new points in an iterative way according to some strategy like improving the accuracy of meta-model itself or finding the optimal design point in the design space. In this work, a hybrid of these two strategies is used, with the aim to achieve both meta-model accuracy and optimum design solution while keeping low the experimental effort. The proposed methodology is applied to an industrial case study. The pragmatism of such hybrid strategy, together with simplicity of implementation promotes the generalization of this approach to other industrial experiments.展开更多
文摘Since the formal introduction of computer experiments back in 1989, substantial work has been done to make these experiments as efficient and effective as possible. As a consequence, more and more industrial studies are performed. In this direction, sequential strategies have been introduced with the aim of reducing the experimental effort while keeping the required accuracy of the meta-model. The strategies consist in building a fairly accurate meta-model based on a low number of experimental points, and then adding new points in an iterative way according to some strategy like improving the accuracy of meta-model itself or finding the optimal design point in the design space. In this work, a hybrid of these two strategies is used, with the aim to achieve both meta-model accuracy and optimum design solution while keeping low the experimental effort. The proposed methodology is applied to an industrial case study. The pragmatism of such hybrid strategy, together with simplicity of implementation promotes the generalization of this approach to other industrial experiments.
