Abstract
Over the past few years, natural fiber composites have been a strategy of rapid growth. The computational methods have become a significant tool for many researchers to design and analyze the mechanical properties of these composites. The mechanical properties such as rigidity, effects, bending, and tensile testing are carried out on natural fiber composites. The natural fiber composites were modeled by using some of the computation techniques. The developed convolutional neural network (CNN) is used to accurately predict the mechanical properties of these composites. The ground‐truth information is used for the training process attained from the finite element analyses below the plane stress statement. After completion of the training process, the developed design is authorized using the invisible data through the training. The optimum microstructural model is identified by a developed model embedded with a genetic algorithm (GA) optimizer. The optimizer converges to conformations with highly enhanced properties. The GA optimizer is used to improve the mechanical properties to have the soft elements in the area adjacent to the tip of the crack.
Highlights
In the current situation, industries were mostly focused on the sustainable idea of manufacture to minimize the nonrenewable resources procedure and adopt the process of ecofriendly materials by waste reuse or recycling
X and Y are determined as weights, and X and Y weights are regulated by the training process to suit every physical property
Since composite gratings are of 8 × 8 and 16 × 16 sizes, they standardize for every model for all 3 properties of fiber material. e determination of the resulting coefficient (D2) for the adjusted line is 0.916. en, the mean absolute error ratio represents the limitations and generalization issues by the linear model; the modulus exceeds the mean absolute error percentage (25%), and the toughness and strength are as high as 200% and 40%
Summary
Industries were mostly focused on the sustainable idea of manufacture to minimize the nonrenewable resources procedure and adopt the process of ecofriendly materials by waste reuse or recycling. Researchers in the field of materials engineering are investigating these computational methods that may be used to model and optimize the many properties of composite materials reinforced with natural fibers. E use of current computational approaches for studying the characteristics of NFCs has proven to be helpful in the modeling and optimization of composite materials. E shielding process of mechanical properties on natural fiber composites is done by using the electromagnetic interference (EMI). A convolutional neuronal network (CNN) model makes it possible to quantitatively predict the mechanical properties of the compound through maximum fraction space volume [7]. Using an optimization approach depends on GA with the model CNN created for optimizing mechanical properties in terms of stiff and soft material volume fraction and spatial distribution in the microstructure.
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