Investigating the mechanical performance deterioration of Mediterranean cellulosic cypress and pine/polyethylene composites

The synergy of the materials physical characteristics, performance and recyclability become vital for industrial sustainability. However, finding a suitable cellulosic fiber type to form potential cellulosic-based composite and investigating performance deteriorations are of paramount importance to expand sustainable design possibilities for various applications. In this work investigations of the mechanical performance deterioration of both Mediterranean cellulosic pine and cypress fibers are experimentally investigated. This was achieved by utilizing the fibers with polyethylene matrix to reveal their potential capabilities for industrial applications. Numerous composites with various parameters like fiber types, fiber loading, fiber size, and reinforcement conditions were designed to study several characteristics of the cellulosic composites, their mechanical performance deteriorations, as well as determining the optimal fiber loading condition for each particular studied mechanical property of the composites. Results demonstrate that mechanical properties are significantly changed with fiber loading. In addition, the failure mode in the high fiber loading composites is an obvious indication of the improper or ineffective load transfer between the matrix and the cellulosic fiber. Moreover, it is revealed here that the performance of cypress fibers with polyethylene matrix is much better than that of pine for the considered properties with reference to the neat polyethylene matrix. The overall performance of both types of fibers with polyethylene clearly demonstrates that the performance of cypress fibers is much better than that of pine for all considered properties.     

Mechanical performance,thermal stability and morphological analysis of date palm fiber reinforced polypropylene composites toward functional bio-products

Cellulose - Sustainable bio-materials are now potential alternatives for synthetic composites to achieve more functional green products. However, the microstructure-performance synergy is critical...     

A Model for Evaluating and Determining the Most Appropriate Polymer Matrix Type for Natural Fiber Composites

The process of determining the proper polymer matrix type, using a wide range of criteria, to form a natural fiber–reinforced polymer composite is still not established enough. This work introduces, for the first time, a model to select the proper polymer matrix type for natural fibers to enhance the sustainability of the automotive industry. The model was developed to rank different polymers and to determine their relative merits considering 20 different criteria simultaneously, including different physical, mechanical, chemical, environmental, and technical criteria. This work can support establishing a road map for proper selection of polymers in different engineering applications as well as increasing the reliability of the polymer selection process.     

A novel adaptive neuro-fuzzy inference system model to predict the intrinsic mechanical properties of various cellulosic fibers for better green composites

In this work a novel adaptive neuro-fuzzy inference system model has been developed for the prediction of the intrinsic mechanical properties of various cellulosic natural fibers to enhance their selection for better green composite materials. The model combined modeling function of the fuzzy inference system with the learning capability of the artificial neural network. The developed model was built up based on experimental mechanical properties of various cellulosic fiber types commonly used for natural fiber reinforced composites, and the rules have been generated directly from the experimental data. The developed model was capable of predicting all of Young's modulus, ultimate tensile strength, and elongation at break properties from only two intrinsic properties of fibers namely; cellulose and moisture content. The adaptive neural fuzzy inference system (ANFIS) structure included five layers to realize the establishment and calculation of each model. The system architecture included the fuzzy input layer, product layer, normalized layer, de-fuzzy layer and total output layer. Results have been revealed that the model’s predictions were highly in agreement with other experimentally gained properties when compared with experimental results for verifying the approach. The accuracy of the developed model would enhance predicting other cellulosic fiber properties to develop better natural fiber composites in the near future.