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Large amount of wood dust is created as a byproduct of woodworking activities. Every year, there is an increase in wood dust generation, which severely pollutes the environment. Consequently, it becomes imperative to use wood dust in the production of useful products. A Carbonized Wood-Silicon Dioxide Filled Natural Rubber Composite (CWSDFNRC) was therefore created in this work using a compression molding method. The friction and compression properties of the composites were determined. Modeling of the composite's mechanical and friction characteristics was done using artificially intelligent techniques including Response Surface Methodology (RSM), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The novel material was thermally analyzed using Dynamic Mechanical Analysis (DMA), Differential Scanning Calorimetry (DSC), Thermogravimetric Analysis (TGA), and Differential Thermal Analysis (DTA). The effectiveness of RSM, ANN, and ANFIS was demonstrated by relevant error indices. The optimization method revealed the ideal level of fitness at particle size, carbonization temperature, filler content, curing temperature, curing pressure, and curing time of 150 μm, 214 °C, 51 phr, 150 °C, 3 Pa, and 10 min respectively. These fitness conditions gave an optimal value of 17.63 MPa for compressive strength and a friction coefficient of 0.96. The novel material's characteristics contrasted well with those of comparable materials described in the literature, suggesting that it has the potential to be used in the manufacture of outer shoe soles and other elastomeric applications.

Reinforced composite is a preferred choice of material for the design of industrial lightweight structures. As of late, composite materials analysis and development utilizing machine learning algorithms have been getting expanding consideration and have accomplished extraordinary upgrades in both time productivity and expectation exactness. This review encapsulates recent advances in machine learning-based design of reinforced composite during the last half-decade. It summarizes the limitations of traditional methods of reinforced composite development and presents a detailed protocol of machine learning in composite materials technology; implementation of machine learning algorithms in reinforced composite material design was covered, with an emphasis on the importance of data hygiene. Machine learning integration in material and process selection, and data sourcing techniques for machine learning-based design were also examined. The evaluation also looked at emerging digital tools and platforms for implementing machine learning algorithms. In addition, an essential effort was made to identify research gaps and define areas for further research. This review is indeed designed to provide some direction for future research into the use of machine learning for composite material design.

In this study, post-harvest Dioscorea alata stem fiber was rated, extracted and treated with 2 % concentration of Sodium hydroxide (NaOH) for 4 h. Three replicate samples of Dioscorea alata stem fibre/epoxy composites (DASFEC) were prepared as per ASTM D3039 and tested under tension with a crosshead displacement rate of 2 mm/min. The laminate in-plane strains (ex0,ey0,eyx0), out-plane strain due to the bending with the curvatures (kx, ky, kxy), strains (ex, ey, exy) and ply stresses in material axes (σ1, σ2, τ12) were calculated using Classical Laminate Theory. The longitudinal tensile strength, yield strength, shear strength and Elastic modulus of Dioscorea alata stem fiber/epoxy lamina (DASFEL) were respectively estimated as 94.2 MPa, 44.4 MPa, 18.1 MPa and 3552.5 MPa. Fourier Transform Infrared Spectroscopy (FTIR) Analysis captured the O-H present in cellulose II at 2750.3 cm−1, 3001.3 cm−1, 3279.849 cm−1and 3813.16 cm−1 peaks with weak intensity on the average. Energy-Dispersive X-ray Spectroscopy (EDS) analysis showed the high presence of Carbon (C) and Oxygen (O) with small deposit of Silicon (Si). The damage onset for individual ply was detected by both Maximum Stress Theory and Tsai-Hill Theory in ply 2 at 90 degrees while the overal laminate strenght (σx) was estimated as 64.7 MPa. The study established that DASFEC is desirable for car interior panel design.