The numerous advantages of natural fiber reinforced hybrid composites, such as their light weight, biodegradability, recyclability, availability, and low cost, have brought them to the forefront for various structural applications in the automotive and aerospace industries. Aim of this study was to evaluate the impact of varying the weight fractions of bamboo micro filler on the tensile, flexural, impact, and water absorption properties of the Sisal-Bamboo-E-glass fiber reinforced epoxy matrix hybrid composite prepared by manual hand layup method. To enhance the bond between natural and synthetic fibers and reduce the hydrophilic nature of natural fibers, bamboo and sisal fibers were treated with 5 % NaOH for 6 and 8 h, respectively. The E-glass fiber content was kept constant at 10 %, while the weight fractions of sisal, bamboo fiber, epoxy matrix, and bamboo micro filler were varied. The bamboo micro filler size ranged from 75 to 100 μm. Tensile and flexural tests were conducted using a computer-controlled universal electromechanical testing machine, while impact testing was performed with a Charpy impact test machine. The results showed that the hybrid composite containing 15 % sisal, 10 % glass, 15 % bamboo fiber, 57 % epoxy, and 3 % bamboo micro filler exhibited the highest tensile strength (87 MPa),flexural strength (77 MPa), high impact energy (8.9 J) and high toughness (24.64 J/cm²). Conversely, the highest water absorption capacity was observed in composites with 6 % bamboo micro filler. Overall, the tensile, flexural, impact, and water absorption properties of the hybrid composites were significantly influenced by the weight fractions of the fibers and bamboo micro filler.

Currently, hybridizing natural fibers with synthetic fiber is considered as the best solution for reducing the environmental pollution and the cost of materials. This is due to the lower mechanical properties and high water absorption capacity problems of natural fiber reinforced composites and nonbiodegradability and high-cost problem of synthetic fibers that can be solved by hybridizing them. This article aims to investigate the influence of the weight fraction of the sisal fiber on the mechanical property of the E-glass-sisal fiber reinforced epoxy matrix composite. The sisal fiber was treated by 8% sodium hydroxide (NaOH) for three hours. The samples of five different weight fractions of fibers (0%, 20%, 30%, 40%, and 60%) with a constant weight fraction of epoxy matrix (40%) were prepared as per the ASTM standard prepared by the manual hand layup method at room temperature. The tensile, compression, and flexural tests of the samples were carried out on the WP 310 universal testing machine. From the experimental results, it has been observed that the increasing weight fraction of the sisal fiber greatly influences the mechanical properties (tensile, compression, and flexural) of E-glass-sisal hybrid reinforced epoxy matrix composites. From all the tested samples, those consisting of 60% E-glass and 0% sisal fibers with 40% epoxy matrix showed better mechanical properties than other hybrid samples with tensile, compressive, and flexural strengths of 464.03 MPa, 40.1 MPa, and 239.06 MPa, respectively.

This article focusses on predicting the compressive properties of polyurethane-derived carbon foam using an artificial neural network (ANN) approach. To train the model, strain, pore density (20, 40, and 60 ppi), and solvents (acetone, ethanol, and methanol) were used as inputs, while compressive stress was used as output. Categorical variables like acetone, ethanol, and methanol were converted to binary form before training the ANN model by using one-hot encoding mechanism. Both inputs and outputs were normalized to prevent features with larger numerical ranges from dominating the training process. A feed-forward ANN with four hidden layers, each containing 100 neurons, was constructed. The performance of the ANN model was tested using three metrics: mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R²). The Adam optimizer was used to optimize the weights and biases of the ANN. The model was trained for 10,000 epochs with a batch size of 50. Rectified Linear Unit (ReLU) and linear functions were used as activation functions for the hidden layers and the output layer, respectively. From the results, overall average MSE, MAE, RMSE, and R² values of 36.34, 4.42, 6.00, and 0.9797, respectively, were obtained.

This article sought to determine the response of a carbon foam material derived from polyurethane foam when subjected to a quasi-static compression load. The effects of the foam pore densities and additives (solvents) on the compression strength, compressive modulus, and surface morphology of the carbon foam were investigated. In this study, three different carbon foam pore densities (20, 40, and 60 ppi) and three solvents for the phenol–formaldehyde resins that coated the polymer foam (acetone, ethanol, and methanol) were used. Carbon foams were derived from polyurethane foams by carbonization. Quasi-static compression testing was carried out using a universal testing machine. The compressive strength, compressive modulus, and relative density of these different carbon foams were computed and compared. Two-way ANOVA analyses were performed to compare the significance of solvents and pore density. These results showed that pore density and solvents significantly affected the compressive strength, compressive modulus, and surface morphology of the fabricated polyurethane-derived carbon foam. Finally, the maximum compressive strength and maximum compressive modulus were observed in carbon foam (60 ppi) with 40% methanol as the solvent. Conversely, a minimum compressive strength was observed for a 20 ppi carbon foam with a 20% acetone solvent, and a minimum compressive modulus was observed for a 20 ppi foam with 40% methanol. Lastly, the chemical composition of the polyurethane foams was investigated, and these results indicated that the polyurethane-derived carbon foam had 96% carbon atoms after carbonization.

Polyurethane foam (PUF) is widely used in structural and energy-absorbing applications; however, the relationship between its microstructural characteristics and mechanical properties under varying pore densities, strain rates, and solvent used during sample manufacturing is still insufficiently understood. This study investigates the microstructure and mechanical properties of phenol–formaldehyde resin-impregnated polyurethane foam (PFRI-PUF). The main morphological parameters of foam, including pore area and equivalent pore diameter, as well as shape descriptors such as circularity, pore shape anisotropy, and pore anisotropy angle were extracted and compared across foams with different pore densities (20, 40, and 60 PPI) and solvents (acetone, ethanol, and methanol). Mechanical properties including densification strain, plateau stress, energy absorbed, mean crushing stress, peak stress and elastic modulus were analyzed under varying pore densities, strain rates, and solvent conditions. Furthermore, non-linear regression modeling was performed to predict mechanical properties responses for different pore densities, strain rates and solvents. Finally, the effects of pore density, strain rate and solvent on mechanical properties of PFRI-PUF were assessed using two-way ANOVA. The results indicate that pore density is the dominant factor influencing the mechanical properties of PFRI-PUF than both strain rate and solvent type. Furthermore, the multi-response optimization shows that the 60 PPI foam, combined with a strain rate of 3.24 × 10 ⁻⁴ s ⁻¹ and ethanol as the solvent, yields the most optimal overall mechanical performance.