Laser Scanning Thermography for Coating Thickness Inspection

The paper deals with a new approach to laser thermography for the inspection of coating thickness. The approach is based on scanning the specimen surface point by point, using a low-power laser, and recording the temperature responses with an IR camera. A recorded sequence is then transformed into a sequence similar to a flash pulse thermography sequence. Fast Fourier transform was used as a processing technique. The results are compared with a flash pulse thermography measurement. It was shown that the laser thermography measurement provides a higher sensitivity to thickness changes than flash pulse thermography measurement.

A Machine Learning Method Based on 3D Local Surface Representation for Recognizing the Inscriptions on Ancient Stele

It is challenging to extract reliefs from ancient steles due to their rough surfaces which contain relief-like noise such as dents and scratches. In this paper, we propose a method to segment relief region from 3D scanned ancient stele by exploiting local surface characteristics. For each surface point, four points that are apart from the reference point along the direction of the principal curvatures of the point are identified. The spin images of the reference point and the four relative points are concatenated to provide additional local surface information of the reference point. A random forest model is trained with the local surface features and thereafter, used to classify 3D surface point as relief or non-relief. To effectively distinguish relief from the degraded surface region containing relief-like noise, the model is trained using three-class labels consisting of relief, background, and degraded surface region. The initial three-class result obtained from the model is refined using the k-nearest neighbors algorithm, and finally, the degraded region is re-labeled to background region. Experimental results show that the proposed method performed better than the state-of-the-art, SVM-based method with a margin of 0.68%, 3.53%, 2.25%, and 2.36%, in accuracy, precision, F1 score, and SIRI, respectively. When compared with the height- and curvature-based methods, the proposed method outperforms these existing methods with an accuracy, precision, F1 score, and SIRI of over 4%, 20%, 11%, and 12%, respectively.

Facial Asymmetry in Nonsyndromic and Muenke Syndrome–Associated Unicoronal Synostosis: A 3-Dimensional Study Based on Facial Surfaces Extracted From CT Scans

Objective: To quantify soft tissue facial asymmetry (FA) in children with nonsyndromic and Muenke syndrome–associated unicoronal synostosis (NS-UCS and MS-UCS), hypothesizing that MS-UCS presents with significantly larger FA than NS-UCS. Design: Retrospective cohort study. Patients and Methods: Twenty-one children (mean age: 0.6 years; range: 0.1-1.4 years) were included in the study (NS-UCS = 14; MS-UCS = 7). From presurgical computed tomography scans, facial surfaces were constructed for analysis. A landmark guided atlas was deformed to match each patient’s surface, obtaining spatially detailed left-right point correspondence. Facial asymmetry was calculated in each surface point across the face, as the length (mm) of an asymmetry vector, with its Cartesian components providing 3 directions. Mean FA was calculated for the full face, and the forehead, eye, nose, cheek, mouth, and chin regions. Results: For the full face, a significant difference of 2.4 mm ( P = .001) was calculated between the 2 groups, predominately in the transverse direction (1.5 mm; P < .001). The forehead and chin regions presented with the largest significant difference, 3.5 mm ( P = .002) and 3.2 mm ( P < .001), respectively; followed by the eye (2.4 mm; P = .004), cheek (2.2 mm; P = .004), nose (1.7 mm; P = .001), and mouth (1.4 mm; P = .009) regions. The transverse direction presented with the largest significant difference in the forehead, chin, mouth, and nose regions, the sagittal direction in the cheek region, and the vertical direction in the eye region. Conclusions: Muenke syndrome–associated unicoronal synostosis presented with significantly larger FA in all regions compared to NS-UCS. The largest significant differences were found in the forehead and chin regions, predominantly in the transverse direction.

Polynomial values of surface point counting polynomials

There are many results in the literature concerning power values, equal values or more generally, polynomial values of lattice point counting polynomials. In this paper, we prove various finiteness results for polynomial values of polynomials counting the lattice points on the surface of an [Formula: see text]-dimensional cube, pyramid and simplex.

Stress Analysis of Multilayered Coatings Subjected to Surface Point Contact Loading Based on Its Three-Dimensional Elastic Field Solution

In order to investigate the effect of the structural layout of multilayered coatings on its mechanical behavior, a three-dimensional elastic field solution is developed for multilayered solids subjected to surface point contact loading, which is converted from the elastic field solution in frequency domain by using a numerical conversion algorithm. The elastic field solution in frequency domain is obtained by numerically solving a group of linear equations involving the unknown constants in the general elastic field solution of layered material that is obtained by using Fourier integral transform technique. The present solution is validated by comparing with the exact analytical solution for uncoated solids and finite element solution for solids coated with 30 layers. Lastly, the effect of structural layout of multilayered coatings is further investigated with present solution. The result shows that the gradient structural layout with elasticity modulus decreasing gradually from the top layer to the substrate, which is preferable to a larger friction coefficient for multilayered solids subjected to surface line contact loading, is preferable for a smaller friction coefficient <0.1 for multilayered solids subjected to surface point contact loading, and the gradient structural layout with elasticity modulus increasing first in the top layers and then decreasing in the bottom layers, which is preferable to a smaller friction coefficient for multilayered solids subjected to surface line contact loading, is preferable for a friction coefficient >0.2.

AUTOMATIC EXTRACTION AND RECOGNITION OF ROAD MARKINGS BASED ON VEHICLE LASER POINT CLOUD

At present, automatic driving technology has become one of the development direction of the future intelligent transportation system. The high high-precision map, which is an important supplement of the on on-board sensors under the condition of shielding or the restriction of observation distance, provides a priori information for high high-precision positioning and path planning of the automatic driving with the level of L3 and above. The position and semantic information of the road markings, such as the absolute coordinates of th e solid line and the bro ken line, are the basic components of the high high-precision map. At present, point cloud data are still one of the most important data source of the high high-precision map. So, how to get road markings information from original point clouds automatically deserve study. In this paper, point cloud is sliced by the mileage of the road, then each slice is projected onto respective vertical section section. Random Sample Consensus (RANSAC) algorithm is applied to establish road surface buffer area . Finally, moving window filtering is used to extract road surface point cloud from road surface buffer area area. On this basis, the road surface point cloud image is transformed into raster image with a certain resolution by using the method of inverse distance weighted interpolation , and the grid image is converted into binary image by using the method of adaptive threshold segmentation based on the integral graph. Then the method of the Euclidean clustering is used to extract the road markings point cloud from the binary image. Characteristic attribute detection is applied to recognize solid line marking from all clusters. Deep learning network framework pointnet++ is applied to recognize remain road markings including guideline, broken line, straight arrow, and right turn arrow.

A Comparison of Stress Intensity Factor Solutions for Surface Cracked Plates

The present paper compares the stress intensity factor (SIF) solutions for surface-cracked plates presented in R6, BS 7910, API 579-1/ASME FFS-1 and RSE-M. Both extended surface cracks and semi-elliptical surface cracks are considered. In general, the agreement between the various solutions is found to be good at within 5%. However, particular cases are highlighted which are associated with larger differences between the SIFs. For extended surface cracks, the various solutions agree closely at within 5% in all but one case. For semi-elliptical surface cracks, the agreement between the various solutions is generally better at the deepest point than at the so-called ‘surface’ points. In particular, the surface point SIFs corresponding to the Vainshtok and Varfolomeyev weight function solution which appears in API-579-1/ASME FFS-1 differ significantly from the other solutions in some cases. It is concluded that further work is required in order to establish an appropriate definition of the surface point SIF for use in structural integrity calculations.