Search Results (13)

Cracks at the corners of casting billets have a small morphology and rough surfaces. Corner cracks are generally irregular, with a depth of about 0.2–5 mm and a width of about 0.5–3 mm. It is difficult to detect the depth of cracks and the three-dimensional morphological characteristics. The severity of cracks is hard to evaluate with traditional inspection methods. To effectively extract the topographic features of corner cracks, a multi-scale surface crack feature extraction algorithm, based on weighted adaptive gradient descent, was proposed. Firstly, the point cloud data of the corners of the billet were collected by the three-dimensional visual inspection platform. The point cloud neighborhood density was calculated using the k-nearest neighbor method; then the weighted covariance matrix was used to calculate the normal rate of change. Secondly, the adaptive attenuation rate, based on normal change, was fused with the density weight, which can calculate the Gaussian weight in regard to the neighborhood. Gaussian weights were used to obtain the gradient changes between point clouds to acquire the multi-scale morphological features of the crack. Finally, the interference caused by surface and boundary effects was eliminated by DBSCAN density clustering. The complete three-dimensional morphology characteristics of the crack were obtained. The experimental results reveal that the precision rate, recall rate, and F-value of the improved algorithm are 96.68%, 91.32%, and 93.92%, respectively, which are superior to the results from the RANSAC and other mainstream algorithms. The three-dimensional morphological characteristics of corner cracks can be effectively extracted using the improved algorithm, which provides a basis for judging the severity of the defect. Full article

In the assessment of pipeline integrity using magnetic flux leakage (MFL) detection, it is crucial to quantify defects accurately and efficiently using MFL signals. However, in complex detection environments, traditional defect inversion methods exhibit low quantification accuracy and efficiency due to the complexity of their algorithms or excessive reliance on a priori knowledge and expert experience. To address these issues, this study presents a novel defect quantification method based on multi-sensor signal fusion (MSSF). The method employs a multi-sensor probe to fuse the MFL signals under multiple lift-off values, enhancing the diversity of defect information. This enables defect-opening profile recognition using the characteristic approximation approach (CAA). Subsequently, the MSSF method is based on a 3D magnetic dipole model and integrates the structural features of multi-sensor probes to develop an algorithm. This algorithm iteratively determines the defect depth at multiple data acquisition points within the defect region to obtain the maximum defect depth. The feasibility of the MSSF quantification method is validated through finite element simulation and physical experiments. The results demonstrate that the proposed method achieves accurate defect quantification while enhancing efficiency, with the number of iterations for each defect depth calculation point consistently requiring fewer than 15 iterations. For rectangular metal loss, perforation, and conical defects, quantification errors are less than 10%, meeting practical inspection requirements. Full article

This study presents a process for the quantitative investigation of thinning defects occurring in the containment liner plate (CLP) of a nuclear power plant according to various depths with a combined thermal wave signal and image processing in a lock-in thermography (LIT) technique. For that, a plate sample with a size of 300 × 300 mm was produced considering the 6 mm thickness applied to an actual CLP. The sample was designed with nine thinning defects on the back side with defect sizes of 40 × 40 mm and varying thinning rates from 10% to 90%. LIT experiments were conducted under various modulation frequency conditions, and phase angle data was calculated and evaluated through four-point method processing. The calculated phase angle was correlated with the defect depth. Then, the phase image was binarized by the Otsu algorithm to evaluate defect detection ability and shape. Furthermore, the accuracy of defect depth assessment was evaluated through third-order polynomial curve fitting. The detectability was analyzed by comparing the number of pixels of the thinning defect in the binarized image and the theoretical calculation. Finally, it was concluded that LIT can be applied for fast thinning defect detection and accurate thinning depth evaluation. Full article

The dual oil circuit centrifugal fuel nozzle is made of martensitic stainless steel, which has complex morphological characteristics. The surface roughness characteristics of the fuel nozzle directly affect the degree of fuel atomization and the spray cone angle. The surface characterization of the fuel nozzle is investigated by the fractal analysis method. A sequence of images of an unheated treatment fuel nozzle and a heated treatment fuel nozzle are captured by the super-depth digital camera. The 3-D point cloud of the fuel nozzle is acquired by the shape from focus technique, and its three-dimensional (3-D) fractal dimensions are calculated and analyzed by the 3-D sandbox counting method. The proposed method can characterize the surface morphology well, including the standard metal processing surface and the fuel nozzle surface, and the experiments show that the 3-D surface fractal dimension is positively correlated with the surface roughness parameter. The 3-D surface fractal dimensions of the unheated treatment fuel nozzle were 2.6281, 2.8697, and 2.7620, compared with the heated treatment fuel nozzles dimensions of 2.3021, 2.5322, and 2.3327. Thus, the 3-D surface fractal dimension value of the unheated treatment is larger than that of the heated treatment and is sensitive to surface defects. This study indicates that the 3-D sandbox counting fractal dimension method is an effective method to evaluate the fuel nozzle surface and other metal processing surfaces. Full article

The appearance and meat quality of Penaeus vannamei are important indexes in the production process, and the quality of the product will be reduced if the defective shrimp is mixed in during processing. In order to solve this problem, a quality detection model of Penaeus vannamei based on deep learning was put forward. Firstly, the self-made dataset of Penaeus vannamei was expanded to enhance the generalization ability of the neural network. Secondly, the backbone of YOLOv5 (you only look once v5) is replaced by the lightweight network PP-LCNet that removes the dense layer at the end, which reduces the model parameters and calculation. Then, the 7 × 7 convolution DepthSepConv module is embedded in a PP-LCNet backbone, which effectively strengthens the feature extraction ability of the network. Ultimately, SiLU activation function is used to replace the Hardsigmoid and Hardswish activation functions in the PP-LCNet backbone to enhance the regularization ability and detection speed of the network. Through comparative experiments, the all-round performance of the Shrimp-YOLOv5s network is higher than the current mainstream classical model and the lightweight model. The mAP@0.5, mAP@0.5:0.95, detection speed, parameters, and calculation of Shrimp-YOLOv5s are 98.5%, 88.1%, 272.8 FPS (frames per second), 4.8 M, and 9.0 GFLOPs (giga floating point operations) respectively. Full article

Opportune sensing of ammonia (NH₃) gas is industrially important for avoiding hazards. With the advent of nanostructured 2D materials, it is felt vital to miniaturize the detector architecture so as to attain more and more efficacy with simultaneous cost reduction. Adaptation of layered transition metal dichalcogenide as the host may be a potential answer to such challenges. The current study presents a theoretical in-depth analysis regarding improvement in efficient detection of NH₃ using layered vanadium di-selenide (VSe₂) with the introduction of point defects. The poor affinity between VSe₂ and NH₃ forbids the use of the former in the nano-sensing device’s fabrications. The adsorption and electronic properties of VSe₂ nanomaterials can be tuned with defect induction, which would modulate the sensing properties. The introduction of Se vacancy to pristine VSe₂ was found to cause about an eight-fold increase (from −012 eV to −0.97 eV) in adsorption energy. A charge transfer from the N 2p orbital of NH₃ to the V 3d orbital of VSe₂ has been observed to cause appreciable NH₃ detection by VSe₂. In addition to that, the stability of the best-defected system has been confirmed through molecular dynamics simulation, and the possibility of repeated usability has been analyzed for calculating recovery time. Our theoretical results clearly indicate that Se-vacant layered VSe₂ can be an efficient NH₃ sensor if practically produced in the future. The presented results will thus potentially be useful for experimentalists in designing and developing VSe₂-based NH₃ sensors. Full article

In order to solve the problem that digging lotus roots manually was high in labor intensity, low in efficiency and easy to damage lotus roots, and, in view of the defects of the high cost of existing digging lotus roots equipment and the cumbersome operating process needing a certain experience in technology, a jetting spin type digging lotus root machine is designed. Combined with the mechanism of hydraulic digging lotus root, a rotation pipeline is designed, so as to simplify the whole machine structure and increase the digging width. It takes only manpower or vehicles to push forward to perform digging lotus root work, with simple operation and low manufacturing cost. Pro/Engineer 3D design software and CAD 2D drawing software are used to design key working parts such as rotation pipeline, spray-head, nozzle, etc. In addition, key parameters of nozzle structure are designed. Through validation, the intensity of jet flow impact force produced by the diameter 17 mm nozzle to lotus root surface is 97.22 N, and the pressure is 0.20 MPa, which produces no damage to lotus root. By means of a mechanical analysis method, combined with CFD flow field analysis and fluid dynamics analysis, the rotational speed mathematical model of rotation pipeline in water is established through MATLAB software solving and calculating. In addition, the influence of nozzle structural parameters on rotational speed of rotation pipeline is made clear. By using the kinematic analysis method, the rotational speed mathematical model of rotation pipeline associated with the jetting impact frequency of single point and the time of each impact is established, and from which the restricting factors of the working speed of jetting spin type digging lotus root machine is obtained, so as to improve the efficiency of digging lotus roots machine under the premise of ensuring the digging depth and the quality of lotus roots. Through the CFD flow field analysis and dynamic analysis, the mathematical model of loss power of rotating pipeline is established. Through comprehensive analysis, the mathematical model of working speed associated with digging time and speed of rotary pipe and effective range radius of jet impact for digging lotus root machine is obtained, which provides a theoretical basis for adjustment of working parameters of digging lotus root machine. Full article

This research proposes an analytical method for the prediction of molten pool size in laser-based powder bed fusion (LPBF) additive manufacturing with the consideration of process conditions-dependent absorptivity. Under different process conditions, the melting modes in LPBF are different, which induces the difference in laser absorptivity. An empirical model of absorptivity was used to calculate the laser absorptivity under various process conditions. An analytical point-moving heat source model was employed to calculate the temperature distribution of the build-in LPBF, with absorptivity, material properties, and process conditions as inputs. The molten pool width, length, and depth were determined by comparing the predicted temperature profile with the melting temperature of the material. To validate the proposed method, the predicted molten pool width, and depth of Ti6Al4V were compared with the reported experimental measurements under various process conditions. The predicted molten pool widths were very close to the measured results, and the predictions of molten pool depth were also acceptable. The computational time of the presented model is less than 200s, which shows better computational efficiency than most methods based on numerical iterations, such as the finite element method (FEM). The sensitivity of molten pool width and depth to normalized enthalpy w also discussed. The presented analytical method can be a potential tool for the research of molten pool size and related defects in LPBF. Full article

With the age of pipeline and increase in the volume of urban sewage, the pipeline has different degrees of defects, which can cause safety problems such as road collapse and urban flooding. The service life of drainage pipes is closely related to daily maintenance and inspection, so it is very important to inspect the defects and monitor the operation of drainage pipes regularly. However, the existing research lacks quantitative detection and intelligent management of pipeline defect information. Therefore, the depth camera is used as the sensor to quantitatively detect the volume and area of the pit on the concrete pipe, and a defect information management platform is constructed in this paper. Firstly, combined BIM model with 3D point cloud, this paper proposes a 3D defect information management platform of drainage pipeline. Then, the depth camera is used to collect the damage data and preprocess the data, and a method for calculating the damage volume and surface area of drainage pipeline based on 3D mesh reconstruction of the defect point cloud is proposed. The verification experiment results show that the error between the quantized volume and the real volume is mostly within 10%, and the maximum error is 17.54%, indicating high accuracy. The drainage pipeline information model is created. Finally, the data is uploaded to the information management platform to realize the visualization and informatization of pipeline defects and the later operation and maintenance requirements of the pipeline. Full article

At present, feature-based 3D reconstruction and tracking technology is widely applied in the medical field. In minimally invasive surgery, the surgeon can achieve three-dimensional reconstruction through the images obtained by the endoscope in the human body, restore the three-dimensional scene of the area to be operated on, and track the motion of the soft tissue surface. This enables doctors to have a clearer understanding of the location depth of the surgical area, greatly reducing the negative impact of 2D image defects and ensuring smooth operation. In this study, firstly, the 3D coordinates of each feature point are calculated by using the parameters of the parallel binocular endoscope and the spatial geometric constraints. At the same time, the discrete feature points are divided into multiple triangles using the Delaunay triangulation method. Then, the 3D coordinates of feature points and the division results of each triangle are combined to complete the 3D surface reconstruction. Combined with the feature matching method based on convolutional neural network, feature tracking is realized by calculating the three-dimensional coordinate changes of the same feature point in different frames. Finally, experiments are carried out on the endoscope image to complete the 3D surface reconstruction and feature tracking. Full article

The reliability of the ultrasonic phased array total focusing method (TFM) imaging of parts with curved geometries depends on many factors, one being the probe standoff. Strong artifacts and resolution loss are introduced by some surface profile and standoff combinations, making it impossible to identify defects. This paper, therefore, introduces a probe standoff optimization method (PSOM) to mitigate such effects. Based on a point spread function analysis, the PSOM algorithm finds the standoff with the lowest main lobe width and side lobe level values. Validation experiments were conducted and the TFM imaging performance compared with the PSOM predictions. The experiments consisted of the inspection of concave and convex parts with amplitudes of 0, 5 and 15 λ_Al, at 12 standoffs varying from 20 to 130 mm. Three internal side-drilled holes at different depths were used as targets. To investigate how the optimal probe standoff improves the TFM, two metrics were used: the signal-to-artifact ratio (SAR) and the array performance indicator (API). The PSF characteristics predicted by the PSOM agreed with the quality of TFM images. A considerable TFM improvement was demonstrated at the optimal standoff calculated by the PSOM. The API of a convex specimen’s TFM was minimized, and the SAR gained up to 13 dB, while the image of a concave specimen gained up to 33 dB in SAR. Full article

Graphene with atomic thickness possesses excellent mechanical and electrical properties, which hold great potential for high performance pressure sensing. The exposed electron of graphene is always cross-sensitive to any pollution absorbed or desorbed on the surface, from which the long-term stability of the graphene pressure sensor suffers a lot. This is one of the main obstacles towards graphene commercial applications. In this paper, we utilized polymethylmethacrylate (PMMA)/graphene heterostructure to isolate graphene from the ambient environment and enhance its strength simultaneously. PMMA/graphene pressure sensors, with the finite-depth cavities and the through-hole cavities separately, were made for comparative study. The through-hole device obtained a comparable sensitivity per unit area to the state of the art of the bare graphene pressure sensor, since there were no leaking cracks or defects. Both the sensitivity and stability of the through-hole sensor are better than those of the sensor with 285-nm-deep cavities, which is due to the sealed gas effect in the pressure cavity. A modified piezoresistive model was derived by considering the pressure change of the sealed gas in the pressure cavity. The calculated result of the new model is consistent with the experimental results. Our findings point out a promising route for performance optimization of graphene pressure sensors. Full article

Kinetic Monte Carlo (kMC) methods have been used extensively for the study of crystal dissolution kinetics and surface reactivity. A current restriction of kMC simulation calculations is their limitation in spatial system size. Here, we explore a new and very fast method for the calculation of the reaction kinetics of a dissolving crystal, capable of being used for much larger systems. This method includes a geometrical approach, the Voronoi distance map, to generate the surface morphology, including etch pit evolution, and calculation of reaction rate maps and rate spectra in an efficient way, at a calculation time that was about 1/180 of the time required for a kMC simulation of the same system size at one million removed atoms. We calculate Voronoi distance maps that are based on a distance metric corresponding to the crystal lattice, weighted additively in relation to stochastic etch pit depths. We also show how Voronoi distance maps can be effectively parameterized by kMC simulation results. The resulting temporal sequences of Voronoi maps provide kinetic information. By comparing temporal sequences of kMC simulation and Voronoi distance maps of identical etch pit distributions, we demonstrate the opportunity of making specific predictions about the dissolution reaction kinetics, based on rate maps and rate spectra. The dissolution of an initially flat Kossel crystal surface served as an example to show that a sequence of Voronoi calculations can predict dissolution kinetics based on the information about the distribution of screw defects. The results confirm that a geometrical relationship exists between the material flux from the surface at a certain point and the distance (or, when considering anisotropy, a function of distance) to the nearest defect. In this study, for the sake of comparability, the calculations are made using input parameters directly derived from the kMC models operating at the atomic scale. We show that, using values of v(r_pit) and weighting factors obtained by kMC, the resulting surface morphologies and material flux are almost identical. This implies that discrete Voronoi calculations of starting and end points of the dissolution are sufficient to calculate material flux maps, without the time-consuming overhead of computing the interim reactions at the atomic-scale. This opens a promising new venue to efficiently upscale full-atomic kMC models to the continuum macroscopic level where reactive transport and Lattice Boltzmann calculations can be applied. Full article