Fiber-fabric composites have the characteristics of lighter texture, higher strength, higher damage resistance, better toughness, and flexibility than laminated composites, but their complex structures have not been well studied. This paper is aimed at study the complex structure of woven fabrics in fiber art creation based on multisensor Internet of Things technology and at studying the impact of its composite material mechanical properties. In this paper, it is proposed to use glass fibers and carbon fibers to weave the required structural preforms in a two-dimensional braiding machine and then use the obtained preforms and epoxy resin to prepare three-dimensional two-dimensional braided composite materials with different structures through a molding process. The composites were tested in tension, bending, and compression to obtain data and their failure modes, and the data were compared to obtain correlations. The experimental results in this paper showed that through the tensile, bending, and compression tests of the three kinds of hybrid structure braided composites, the tensile properties of the glass fiber braided composites decreased by about 77%; the influence of angle on the bending properties of carbon fiber braided composites has the largest downward trend of 63%. Two-dimensional biaxial and two-dimensional triaxial preforms are made by weaving glass fiber and carbon fiber, and the angle deviation is basically kept within 2.2%.
With people’s higher and higher spiritual requirements, fiber art is more common in people’s lives. From ordinary fiber materials to handicrafts and daily necessities, there are shadows of fiber art. This paper aims to study the application of two-dimensional images in the visual and tactile dimensions of fiber art design. This paper proposes a three-dimensional simulation of two-dimensional images of art based on deep learning technology to achieve the purpose of convenient display and also analyzes the feature extraction in fiber art. In order to comprehensively compare the performance of the algorithms, this paper compares the performance of four categories: reconstruction speed, number of reconstructed point clouds, diversity of reconstructable categories, and reconstruction stability. The experimental results show that the number of point clouds obtained by the algorithm proposed in this paper far exceeds the other two reconstruction processes, and the average time is 34.4 min. This also shows that such algorithmic processes are more robust to clothing diversity.
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