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were enhanced by 52.10%, 18.78%, and 166.60%, respectively, when the outermost pineapple leaf fiber layers were superseded with ramie fabric.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.

Dataset of the lab-scale 3-axis winding machine integrated with the portable real-time winding angle measurement system.

This article presents three datasets related to the laboratory scale 3-axis filament winding machine. The winding experimental tests are described on the range of winding angle, winding accuracy of programmed G-codes, and linear and rotation speeds in raw data. The real-time winding angle measurement system is developed to monitor and measure the winding angle of filament-wound carbon-fiber reinforced plastics (CFRP) tubes. Two winding patterns are provided as dry and wet winding processes. Moreover, an experimental test of a real-time winding angle measurement system is captured and analyzed. The i-winder app controls the winding machine through a Bluetooth module, which is programmed by MIT App Inventor. The data presented in this article can have a benchmark for developing a multi-axis filament winding machine. It is provided an inexpensive and open-source control system and is embedded in a real-time winding angle measurement system. The experimental assessment data can be found in this article [1]. The data is available in the cloud-based Mendeley Data repository [2].

Potentiality of MWCNT on 3D-printed bio-inspired spherical-roof cubic core under quasi-static loading.

Sandwich panel is increasingly used as lightweight energy absorbing components, which provides excellent crashworthiness performance with the three-dimensional periodic core. This paper investigates 3D-printed bio-inspired spherical-roof cubic cores with multi-walled carbon nanotubes (MWCNT) and foam-filled cores under quasi-static loading. The proposed bio-inspired spherical-roof cubic cores with 1.5 mm wall thickness were manufactured using the fused filament fabrication process, which used 70% polylactic acid (PLA) and 30% carbon fiber filament. Moreover, four groups of 3D-printed bio-inspired spherical-roof cubic cores were compared and analyzed on compressive properties and failure behavior. Experimental results were shown that foam-filled double bio-inspired spherical-roof cubic core with MWCNT was the maximum Fpeak with 1.92 kN, which provided a much more stable plateau load and better energy-absorbing characteristics. In addition, it is conducted that a double bio-inspired spherical-roof cubic core with four notches core is considered as the potential energy-absorbing core.

Progressive Damage Behaviour Analysis and Comparison with 2D/3D Hashin Failure Models on Carbon Fibre-Reinforced Aluminium Laminates.

It is known that carbon fibre-reinforced aluminium laminate is the third generation of fibre metal materials. This study investigates the response of carbon fibre-reinforced aluminium laminates (CARALL) under tensile loading and three-point bending tests, which evaluate the damage initiation and propagation mechanism. The 2D Hashin and 3D Hashin VUMAT models are used to analyse and compare each composite layer for finite element modelling. A bilinear cohesive contact model is modelled for the interface failure, and the Johnson cook model describes the aluminium layer. The mechanical response and failure analysis of CARALL were evaluated using load versus deflection curves, and the scanning electron microscope was adopted. The results revealed that the failure modes of CARALL were mainly observed in the aluminium layer fracture, fibre pull-out, fracture, and matrix tensile fracture under tensile and flexural loading conditions. The 2D Hashin and 3D Hashin models were similar in predicting tensile properties, flexural properties, mechanical response before peak load points, and final failure modes. It is highlighted that the 3D Hashin model can accurately reveal the failure mechanism and failure propagation mechanism of CARALL.