Abstract
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 >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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.