Framework for Creating Digital Representations of Structural Components Using Computational Intelligence Techniques

A framework for creating a digital representation of physical structural components is investigated. A model updating scheme used with an artificial neural network to map updating parameters to the error observed between simulated experimental data and an analytical model of a turbine-engine fan blade. The simulated experimental airfoil has as-manufactured geometric deviations from the nominal, design-intent geometry on which the analytical model is based. The manufacturing geometric deviations are reduced through principal component analysis, where the scores of the principal components are the unknown updating parameters. A range of acceptable scores is used to devise a design of computer experiments that provides training and testing data for the neural network. This training data is composed of principal component scores as inputs. The outputs are the calculated errors between the analytical and experimental predictions of modal properties and frequency-response functions. Minimizing these errors will result in an updated analytical model that has predictions closer to the simulated experimental data. This minimization process is done through the use of two multiobjective evolutionary algorithms. The goal is to determine if the updating process can identify the principal components used in simulating the experiment data.