The parameter identification of failure models for composite plies can be cumbersome,
due to multiple effects as the consequence of brittle fracture. Our work proposes
an
iterative, nonlinear design of experiments (DoE) approach that finds the most
informative experimental data to identify the parameters of the Tsai-Wu, Tsai-Hill,
Hoffman, Hashin, max stress and Puck failure models. Depending on the data, the
models perform differently, therefore, the parameter identification is validated by
the
Euclidean distance of the measured points to the closest ones on the nominal surface.
The resulting errors provide a base for the ranking of the models, which helps to
select
the best fitting. Following the validation, the sensitivity of the best model is calculated
through partial differentiation, and a theoretical surface is generated. Lastly, an
iterative
design of experiments is implemented to select the optimal set of experiments from
which the parameters can be identified from the least data and minimizing the fitting
error. This way, the number of experiments that is required for the model identification
a composite material may be significantly reduced.
We demonstrate how the proposed method selected the most optimal experiments
through procedurally generated data. The results indicate that if the dataset contains
enough information the method is robust and accurate. If the data set lacks the
necessary information, novel material tests can be proposed based on the optimal
points of the parameters' sensitivity of the generated failure model surface.
