Machine learning-based strain-load prediction of high-performance 3D woven fabrics

Abstract

Strain-load is a critical aspect of high-performance textile composites, and testing and application of these materials require significant time, numerous samples, as well as extensive physical experimentation. To address these challenges, this study proposes the construction of machine learning prediction models as an alternative to traditional methods for assessing the strain-load performance of 3D woven fabrics. Four algorithms of Random Forest, Ridge Regressor, K-Nearest Neighbor, and Multi-layer Perceptron were investigated with different feature extraction strategies to predict the strain-load curves of 3D woven fabrics for both warp and weft directions as a whole. A total of 62 datasets sourced from the literature were investigated with 5-fold cross validation in model construction. The results indicate that the strain-load curves can be effectively simulated by different machine learning models basically, and more data with better feature extraction is expected to promote the prediction performance of constructed models in more application cases in future work.