  Modeling open hole failure of composites is a complex task, consisting in a
highly nonlinear response with interacting failure modes. Numerical modeling of
this phenomenon has traditionally been based on the finite element method, but
requires to tradeoff between high fidelity and computational cost. To mitigate
this shortcoming, recent work has leveraged machine learning to predict the
strength of open hole composite specimens. Here, we also propose using
data-based models but to tackle open hole composite failure from a
classification point of view. More specifically, we show how to train surrogate
models to learn the ultimate failure envelope of an open hole composite plate
under in-plane loading. To achieve this, we solve the classification problem
via support vector machine (SVM) and test different classifiers by changing the
SVM kernel function. The flexibility of kernel-based SVM also allows us to
integrate the recently developed quantum kernels in our algorithm and compare
them with the standard radial basis function (RBF) kernel. Finally, thanks to
kernel-target alignment optimization, we tune the free parameters of all
kernels to best separate safe and failure-inducing loading states. The results
show classification accuracies higher than 90% for RBF, especially after
alignment, followed closely by the quantum kernel classifiers.
