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.

翻译：复合材料开孔失效的建模是一项复杂任务，涉及高度非线性响应及相互作用的失效模式。传统上，这一现象的数值建模通常基于有限元方法，但需要在高保真度与计算成本之间进行权衡。为弥补这一不足，近期研究利用机器学习预测开孔复合材料试件的强度。本文同样提出采用基于数据的模型，但从分类角度解决开孔复合材料失效问题。具体而言，我们展示了如何训练代理模型来学习面内载荷作用下开孔复合材料板的最终失效包络线。为此，通过支持向量机（SVM）求解分类问题，并通过改变SVM核函数测试不同分类器。基于核的SVM的灵活性还使我们能够将最新发展的量子核集成到算法中，并将其与标准径向基函数（RBF）核进行比较。最后，借助核目标对齐优化，我们调整所有核的自由参数，以最佳区分安全状态与导致失效的载荷状态。结果显示，RBF核的分类准确率超过90%（尤其是在对齐之后），量子核分类器紧随其后。