• Balzano, Luigi
• Pacáková, Barbara
• Knaapila, Matti
• Fossum, Jon Otto
• Kanters, Marc
• Demchenko, Hanna
• Sexton, Alexander Harold

Abstract

<jats:p>The tensile loading history of continuous carbon fiber composites is classified using machine learning (ML) and crystallographic data from the polymer matrix. Composites with polyamide‐4,10 matrix and unidirectional 10° and 45°, and 0°/90° cross‐ply layups are subjected to single‐cycle uniaxial tensile loads corresponding to 25–90% of their nominal maximum strain, and mapped by X‐ray diffraction with approximately 1000 data points from each layup. The unit cell alterations are used as a feature set for optimizing three ML algorithms; linear discriminant analysis, support vector machines (SVM), and gradient‐boosted decision trees (GBDT), with the objective of predicting five discrete loading magnitudes of the respective layups. It is demonstrated that SVMs and GBDTs can be trained to achieve a classification accuracy of &gt;90% on unseen test data, both in cases where the feature set consists of data points from individual layups only, but also when data from the three layups are aggregated. The performance of the models is also shown to be similar to a binary problem, in which the composites are categorized according to a threshold load.</jats:p>

Topics

• impedance spectroscopy
• polymer
• Carbon
• composite
• machine learning