Mechanical Characterization, Water Absorption, and Thickness Swelling of Lightweight Pineapple Leaf/Ramie Fabric-Reinforced Polypropylene Hybrid Composites.

Fiber-reinforced composites are among the recognized competing materials in various engineering applications. Ramie and pineapple leaf fibers are fascinating natural fibers due to their remarkable material properties. This research study aims to unveil the viability of hybridizing two kinds of lignocellulosic plant fiber fabrics in polymer composites. In this work, the hybrid composites were prepared with the aid of the hot compression technique. The mechanical, water-absorbing, and thickness swelling properties of ramie and pineapple leaf fiber fabric-reinforced polypropylene hybrid composites were identified. A comparison was made between non-hybrid and hybrid composites to demonstrate the hybridization effect. According to the findings, hybrid composites, particularly those containing ramie fiber as a skin layer, showed a prominent increase in mechanical strength. In comparison with non-hybrid pineapple leaf fabric-reinforced composites, the tensile, flexural, and Charpy impact strengths were enhanced by 52.10%, 18.78%, and 166.60%, respectively, when the outermost pineapple leaf fiber layers were superseded with ramie fabric. However, increasing the pineapple leaf fiber content reduced the water absorption and thickness swelling of the hybrid composites. Undeniably, these findings highlight the potential of hybrid composites to reach a balance in mechanical properties and water absorption while possessing eco-friendly characteristics.

Experimental and Numerical Investigation Integrated with Machine Learning (ML) for the Prediction Strategy of DP590/CFRP Composite Laminates.

This study unveils a machine learning (ML)-assisted framework designed to optimize the stacking sequence and orientation of carbon fiber-reinforced polymer (CFRP)/metal composite laminates, aiming to enhance their mechanical properties under quasi-static loading conditions. This work pioneers the expansion of initial datasets for ML analysis in the field by uniquely integrating the experimental results with finite element simulations. Nine ML models, including XGBoost and gradient boosting, were assessed for their precision in predicting tensile and bending strengths. The findings reveal that the XGBoost and gradient boosting models excel in tensile strength prediction due to their low error rates and high interpretability. In contrast, the decision trees, K-nearest neighbors (KNN), and random forest models show the highest accuracy in bending strength predictions. Tree-based models demonstrated exceptional performance across various metrics, notably for CFRP/DP590 laminates. Additionally, this study investigates the impact of layup sequences on mechanical properties, employing an innovative combination of ML, numerical, and experimental approaches. The novelty of this study lies in the first-time application of these ML models to the performance optimization of CFRP/metal composites and in providing a novel perspective through the comprehensive integration of experimental, numerical, and ML methods for composite material design and performance prediction.