Predicting Open-Hole Laminates Failure Using Support Vector Machines With Classical and Quantum Kernels

TL;DR

This paper develops machine learning models, including quantum kernels, to predict the failure of open-hole composite plates under load, achieving over 90% accuracy and offering a computationally efficient alternative to traditional finite element methods.

Contribution

It introduces the use of quantum kernels in support vector machines for composite failure prediction, enhancing classification accuracy and model flexibility.

Findings

RBF kernel achieved over 90% accuracy.

Quantum kernels performed comparably to classical kernels.

Kernel-target alignment improved classification performance.

Abstract

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.