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It is the goal that the aerospace industry has been continuously pursuing to meet the lightweight design with excellent mechanical properties. A structure-material integrated design framework is proposed to enhance the load-bearing rate of a spacecraft rib significantly, based on the optimization design theory. The structure-material integrated design framework is realized in two steps by commercial software Altair Solidthinking Inspire. The first step is that topology optimization is performed to a spacecraft rib at the macroscopic scale, with the minimum mass and the constraints of the additive manufacturing process and stress; while the second step is to optimally infill the lattice structure at the microscopic scale by minimizing the mass and constraining the additive manufacturing process and stress. Representative samples for the optimal rib structure are then fabricated by the additive manufacturing technique, and the tensile test is finally carried out to obtained the load-bearing rate for the different samples. The results show that the spacecraft rib's load-bearing rate is increased by 122.73% by the proposed structure-material integrated design framework compared to the traditional one; moreover, it is significantly more efficient than the direct topology optimization and lattice optimization design. The structure-material integrated design framework shown in this study can provide an efficient way to aerospace structures with lightweight and superior mechanical properties.

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Soybean is one of the world’s most consumed crops. As the human population continuously increases, new phenotyping technology is needed to develop new soybean varieties with high-yield, stress-tolerant, and disease-tolerant traits. Hyperspectral imaging (HSI) is one of the most used technologies for phenotyping. The current HSI techniques with indoor imaging towers and unmanned aerial vehicles (UAVs) suffer from multiple major noise sources, such as changes in ambient lighting conditions, leaf slopes, and environmental conditions. To reduce the noise, a portable single-leaf high-resolution HSI imager named LeafSpec was developed. However, the original design does not work efficiently for the size and shape of dicot leaves, such as soybean leaves. In addition, there is a potential to make the dicot leaf scanning much faster and easier by automating the manual scan effort in the original design. Therefore, a renovated design of a LeafSpec with increased efficiency and imaging quality for dicot leaves is presented in this paper. The new design collects an image of a dicot leaf within 20 s. The data quality of this new device is validated by detecting the effect of nitrogen treatment on soybean plants. The improved spatial resolution allows users to utilize the Normalized Difference Vegetative Index (NDVI) spatial distribution heatmap of the entire leaf to predict the nitrogen content of a soybean plant. This preliminary NDVI distribution analysis result shows a strong correlation (R2 = 0.871) between the image collected by the device and the nitrogen content measured by a commercial laboratory. Therefore, it is concluded that the new LeafSpec-Dicot device can provide high-quality hyperspectral leaf images with high spatial resolution, high spectral resolution, and increased throughput for more accurate phenotyping. This enables phenotyping researchers to develop novel HSI image processing algorithms to utilize both spatial and spectral information to reveal more signals in soybean leaf images.