It is very necessary to understand the mechanical properties of Rosa roxburghii fruit for harvesting, packaging, processing, transportation, storage, and related equipment design and improvement. This research was to determine the mechanical properties of R. roxburghii fruits through compressive experiments and finite element simulations and to verify the accuracy of the finite element model. In order to achieve finite element analysis, an accurate three-dimensional (3D) geometric model was reconstructed by applying 3D laser technology and point cloud reconstruction algorithm. The elastic modulus and Poisson's ratio of R. roxburghii fruits were determined to be 4.0 MPa and 0.35 through experiments. The finite element simulation results provided valuable numerical data and visualized stress distribution. The simulation results showed that R. roxburghii fruits were damaged under the loads of 156.2 N (vertical) or 148.3 N (horizontal), with a maximum deformation of 5.0 mm (vertical) or 6.93 mm (horizontal), respectively. In the vertical and horizontal compression posi-
Fruit three-dimensional (3D) model is crucial to estimating its geometrical and mechanical properties and improving the level of fruit mechanical processing. Considering the complex geometrical features and the required model accuracy, this paper proposed a 3D point cloud reconstruction method for the Rosa roxburghii fruit based on a three-dimensional laser scanner, including 3D point cloud generation, point cloud registration, fruit thorns segmentation, and 3D reconstruction. The 3D laser scanner was used to obtain the original 3D point cloud data of the Rosa roxburghii fruit, and then the fruit thorns data were removed by the segmentation algorithm combining the statistical outlier removal and radius outlier removal. By analyzing the effects of five-point cloud simplification methods, the optimal simplification method was determined. The Poisson reconstruction algorithm, the screened Poisson reconstruction algorithm, the greedy projection triangulation algorithm, and the Delaunay triangulation algorithm were utilized to reconstruct the fruit model. The number of model vertices, the number of facets, and the relative volume error were used to determine the best reconstruction algorithm. The results indicated that this model can better reconstruct the actual surface of Rosa roxburghii fruit. The method provides a reference for the related application.
This paper proposes a fault diagnosis method for miniature DC motors (MDCMs) in the presence of the uncertainties caused by material and random factors of the production process. In this method, the probability models of fault multiple features are established based on the advantage criterion of the maximum overall average membership to determine the distribution of fault multiple features. The fault diagnosis algorithm is synthesized to obtain the threshold ranges of fault multiple features according to different confidence levels. Experimental test results are presented and analyzed to validate the efficiency and performance of the proposed fault diagnosis method.
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