A Tensor voting Based Surface Anomaly Classification Approach by Using 3D Point Cloud Data

Advanced 3D scanning technology has been widely used in many industries to collect the massive point cloud data of artifacts for part dimension measurement and shape analysis. Though point cloud data also has product surface quality information, it is challenging to conduct effective surface anomaly classification due to the complex data representation, high-dimensionality, and inconsistent size of the 3D point cloud data within each sample. To deal with these challenges, this paper proposes a tensor voting based approach for anomaly classification of artifact surfaces. A case study based on 3D scanned data obtained from a manufacturing plant shows the effectiveness of the proposed method.

A Multiscale Method for Road Network Extraction from High-Resolution SAR Images Based on Directional Decomposition and Regional Quality Evaluation

Road network is an important part of modern transportation. For the demands of accurate road information in practical applications such as urban planning and disaster assessment, we propose a multiscale method to extract road network from high-resolution synthetic aperture radar (SAR) images, which consists of three stages: potential road area segmentation, preliminary network generation, and road network refinement. Multiscale analysis is implemented using an image pyramid framework together with a fixed-size filter. First, a directional road detector is designed to highlight road targets in feature response maps. Subsequently, adaptive fusion is performed independently at each image scale, followed by a threshold method to produce potential road maps. Then, binary maps are decomposed according to the obtained direction information. For each connected component (CC), quality evaluation is conducted to further distinguish road segments and polynomial curve fitting is adopted as a thinning method. Multiscale information fusion is realized through the weighted sum of road curves. Finally, tensor voting and spatial regularization are employed to generate the final road network. Experiments on three TerraSAR images demonstrate the effectiveness of the proposed algorithm to extract road network completely and correctly.

Fractal and Multifractal Characteristics of Lineaments in the Qianhe Graben and Its Tectonic Significance Using Remote Sensing Images

The distribution and characteristics of geological lineaments in areas with active faulting are vital for providing a basis for regional tectonic identification and analyzing the tectonic significance. Here, we extracted the lineaments in the Qianhe Graben, an active mountainous area on the southwest margin of Ordos Block, China, by using the tensor voting algorithm after comparing them with the segment tracing algorithm (STA) and LINE algorithm in PCI Geomatica Software. The main results show that (1) the lineaments in this area are mostly induced by the active fault events with the main trending of NW–SE, (2) the box dimensions of all lineaments, NW–SE trending lineaments, and NE–SW trending lineaments are 1.60, 1.48, and 1.44 (R2 > 0.9), respectively, indicating that the faults exhibit statistical self-similarity, and (3) the lineaments have multifractal characteristics according to the mass index τ(q), generalized fractal dimension D(q), fractal width (Δα = 2.25), fractal spectrum shape (f(α) is a unimodal left-hook curve), and spectrum width (Δf = 1.21). These results are related to the tectonic activity in this area, where a higher tectonic activity leads to more lineaments being produced and a higher fractal dimension. All of these results suggest that such insights can be beneficial for providing potential targets in reconstructing the tectonic structure of the area and trends of plate movement.

Surface accuracy optimization of mechanical parts with multiple circular holes for additive manufacturing based on triangular fuzzy number

Surface accuracy directly affects the surface quality and performance of mechanical parts. Circular hole, especially spatial non-planar hole set is the typical feature and working surface of mechanical parts. Compared with traditional machining methods, additive manufacturing (AM) technology can decrease the surface accuracy errors of circular holes during fabrication. However, an accuracy error may still exist on the surface of circular holes fabricated by AM due to the influence of staircase effect. This study proposes a surface accuracy optimization approach for mechanical parts with multiple circular holes for AM based on triangular fuzzy number (TFN). First, the feature lines on the manifold mesh are extracted using the dihedral angle method and normal tensor voting to detect the circular holes. Second, the optimal AM part build orientation is determined using the genetic algorithm to optimize the surface accuracy of the circular holes by minimizing the weighted volumetric error of the part. Third, the corresponding weights of the circular holes are calculated with the TFN analytic hierarchy process in accordance with the surface accuracy requirements. Lastly, an improved adaptive slicing algorithm is utilized to reduce the entire build time while maintaining the forming surface accuracy of the circular holes using digital twins via virtual printing. The effectiveness of the proposed approach is experimentally validated using two mechanical models.