One of the essential problems in the measurement of the freeform surfaces on a coordinate measuring machine is to design appropriate sampling plans to improve the industrial practice in terms of the tradeoff between the sampling accuracy and the efficiency. This paper presents a curve network sampling strategy to approximate the measured surface within a required accuracy while minimizing the cost and time for the measurement by adaptively deriving the optimal sampling locations. The method iteratively extracts two sets of iso-planar curves along two different directions on the parts to form a curve network, which is used to reconstruct the measured surfaces based on the Gordon surface fitting method. Two criteria are integrated to determine the locations of the sampled curves in the sampling process, including the surface complexity and the deviation of the reconstructed surfaces from the CAD model. Both the computer simulation and the actual measurement are conducted to verify the superior sampling efficiency of the proposed method to the conventional raster fashion sampling in measuring freeform surfaces.
Contact measurements are significant for surface metrology and can provide highly precise results. However, the point-by-point touch sampling process is less efficient, which seriously limits their applications in manufacture process, especially for the measurement of multi-scale complex workpieces. On the other hand, the lack of high-quality labeled datasets in manufacturing industries prevents advanced supervised learning approaches from modeling and accelerating the measurement process. To address these problems, this paper proposed a highly efficient sparse sampling strategy to accelerate the measurement efficiency and a self-learning based approach to reconstruct precise dense results, that can not only dramatically reduce the number of sampling points but also eliminate the dataset demand to train the reconstruction algorithm. The proposed method can learn the prior of sparse samples and then reconstruct dense accurate measurements with self-supervised behavior based on the optimization process of encoder-decoder convolutional neural networks. Intensive experiments show that the proposed approach outperforms blind interpolated methods and even close to supervised learning approaches.
Objective: Intracranial atherosclerotic disease (ICAD) is a cause of ischemic stroke, with the middle cerebral artery (MCA) being commonly affected. The vulnerable plaque is considered as the cause of stroke. High resolution magnetic resonance imaging (HR-MRI) can help us assess the morphology characteristics and vulnerability of intracranial atherosclerosis plaque. HR-MRI could also provide us such information as microvascular structure of perforating arteries from MCA. The aim of our study was to distinguish the differences in the plaque morphology between symptomatic and asymptomatic MCA stenoses, and the relationship between MCA stenosis and perforating artery. Method: 41 MCAs with symptomatic stenoses and 33 MCAs with asymptomatic stenoses (both between 50% and 99%) were included. The characteristics of plaques and the perforating arteries were evaluated by HR-MRI. Results: Between symptomatic and asymptomatic MCA stenosis groups,vessel morphological characteristics such as the degree of stenosis, remodeling index, wall area index didn’t differ. However, patients with symptomatic MCA stenoses had heavier plaque burden (79.74 ± 1.380 vs 66.72 ± 2.776, P < 0.001). , Symptomatic MCA stenoses showed stronger plaque gadolinium enhancement, including bigger ROI area (4.27 ± 0.266 vs 2.75 ± 0.197 P < 0.001), higher enhancement index (0.46 ± 0.061 vs 0.20 ± 0.067 P = 0.005) and larger enhancement volume (116.34 ± 21.960 vs 35.21 ± 6.535 P < 0.001). With multivariate regression analysis, the plaque burden, the region of interested area, and the enhancement index were independently associated with symptomatic MCA stenosis. The number of perforating arteries of MCAs with moderate to severe stenoses was less than that of normal controls (symptomatic MCA: 3.82 ± 0.165, asymptomatic MCA: 3.85 ± 0.177, normal MCA :4.47±0.162, P=0.006). Conclusions: The plaque burden may be a new index in assessing the vulnerability of intracranial atherosclerosis plaques. The plaque burden, the region of interested area, and the enhancement index are independently associated with symptomatic MCA stenosis. The perforating arteries from symptomatic and asymptomatic MCA stenosis are both less than those from normal MCA controls.
Fluid jet polishing is an emerging process which possesses the advantages of localized force and less heat generation, as well as the stable and controllable material removal function without tool wear. Due to the complex machining mechanism, it is still difficult to model the material removal rate and predict the surface generation for fluid jet polishing. In this article, theoretical and experimental investigation of three-dimensional-structured surface generation by fluid jet polishing has been carried out. A surface topography simulation model is established for predicting the three-dimensional-structured surface generation by fluid jet polishing. A series of polishing experiments have been conducted to optimize the process parameters of fluid jet polishing and the fabrication of three-dimensional-structured surfaces. In terms of the pattern of three-dimensional-structured surfaces generated, the simulation results are found to agree with the experimental results.
We present a new method of angular measurement, which is to rotate a 15-mm-diameter, 3-mm-thick crystal quartz plate in a He-Ne laser cavity to produce a laser mode split. The magnitude of the mode split (in hertz) represents the angle of rotation. The experimental devices used are described. The stability of the beat frequency is 0.78 kHz (1σ). A sensitivity of 2.62 3 × 10(4) Hz/", where" is angular seconds, has been reached and the repeatability is 0.3". The principal error factors are discussed.
Light field (LF) imaging is an advanced visual perception system, which can record the intensity and direction information of light rays and provide multi-viewpoint images from a single capture. However, there is a trade-off between spatial and angular resolutions due to the restricted sensor size, which limits the wide applications of LF cameras. To address this problem, we propose a cooperative network to super-resolve LF sub-aperture images based on the multi-modality fusion. Specifically, in order to fully explore the LF information, we adopt various modalities and extract corresponding features to emphasise diverse LF characteristics. Then, we design a multi-scale fusion module to effectively integrate global and local LF features and apply frequency-aware attention mechanism to adaptively reinforce fused features. Extensive experiments demonstrate the superiority of our method on both qualitative and quantitative evaluations, with competitive execution efficiency.
Although the scanning white light interferometer can provide measurement results with subnanometer resolution, the measurement accuracy is far from perfect. The surface roughness and surface gradient have significant influence on the measurement uncertainty since the corresponding height differences within a single CCD pixel cannot be resolved. This paper presents an uncertainty estimation method for estimating the measurement uncertainty due to the surface gradient of the workpiece. The method is developed based on the mathematical expression of an uncertainty estimation model which is derived and verified through a series of experiments. The results show that there is a notable similarity between the predicted uncertainty from the uncertainty estimation model and the experimental measurement uncertainty, which demonstrates the effectiveness of the method. With the establishment of the proposed uncertainty estimation method, the uncertainty associated with the measurement result can be determined conveniently.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)