Search Results (19)

The article presents a method for detecting low-intensity DDoS attacks, focused on identifying difficult-to-detect “low-and-slow” scenarios that remain undetectable by traditional defence systems. The key feature of the developed method is the statistical criteria’s (χ² and T statistics, energy ratio, reconstruction errors) integration with a combined neural network architecture, including convolutional and transformer blocks coupled with an autoencoder and a calibrated regressor. The developed neural network architecture combines mathematical validity and high sensitivity to weak anomalies with the ability to generate interpretable artefacts that are suitable for subsequent forensic analysis. The developed method implements a multi-layered process, according to which the first level statistically evaluates the flow intensity and interpacket intervals, and the second level processes features using a neural network module, generating an integral blend-score S metric. ROC-AUC and PR-AUC metrics, learning curve analysis, and the estimate of the calibration error (ECE) were used for validation. Experimental results demonstrated the superiority of the proposed method over existing approaches, as the achieved values of ROC-AUC and PR-AUC were 0.80 and 0.866, respectively, with an ECE level of 0.04, indicating a high accuracy of attack detection. The study’s contribution lies in a method combining statistical and neural network analysis development, as well as in ensuring the evidentiary value of the results through the generation of structured incident reports (PCAP slices, time windows, cryptographic hashes). The obtained results expand the toolkit for cyber-attack analysis and open up prospects for the methods’ practical application in monitoring systems and law enforcement agencies. Full article

Three-dimensional bioprinting holds great promise for tissue engineering, but struggles with fabricating complex curved geometries such as vascular networks. Though precise, traditional Cartesian bioprinters are constrained by linear layer-by-layer deposition along fixed axes, resulting in limitations such as the stair-step effect. Multi-axis robotic bioprinting addresses these challenges by allowing dynamic nozzle orientation and motion along curvilinear paths, enabling conformal printing on anatomically relevant surfaces. Although robotic arms offer lower mechanical precision than CNC stages, accuracy can be enhanced through methods such as vision-based toolpath correction. This study presents a modular multi-axis robotic embedded bioprinting platform that integrates a six-degrees-of-freedom robotic arm, a pneumatic extrusion system, and a viscoplastic support bath. A streamlined workflow combines CAD modeling, CAM slicing, robotic simulation, and automated execution for efficient fabrication. Two case studies validate the system’s ability to print freeform surfaces and vascular-inspired tubular constructs with high fidelity. The results highlight the platform’s versatility and potential for complex tissue fabrication and future in situ bioprinting applications. Full article

Optical coherence tomography (OCT) employs light to acquire high-resolution 3D images and is widely applied in fields such as ophthalmology and forensic science. A popular technique for visualizing the top view (en face) is to slice it with flat horizontal plane or apply statistical functions along the depth axis. However, when the target appears as a thin layer, strong reflections from other layers can interfere with the target, rendering the flat-plane approach ineffective. We apply Otsu-based thresholding to extract the object’s foreground, then use least squares (with Tikhonov regularization) to fit a polynomial curve that describes the sample’s structural morphology. The surface is then used to obtain the latent fingerprint image and its residues at different depths from a translucent tape, which cannot be analyzed using conventional en face OCT due to strong reflection from the diffusive surface, achieving FSIM of 0.7020 compared to traditional en face of 0.6445. The method is also compatible with other signal processing techniques, as demonstrated by a thermal-printed label ink thickness measurement confirmed by a microscopic image. Our approach empowers OCT to observe targets embedded in samples with arbitrary postures and morphology, and can be easily adapted to various optical imaging technologies. Full article

Fiber-reinforced composite (FRC) additive manufacturing technologies have successfully overcome the limitations of traditional autoclave forming, offering significantly enhanced design freedom. However, one of the remaining key challenges is the planning of continuous printing paths that align with a defined fiber orientation vector field within FRC structures. This paper introduces a comprehensive framework for multi-axis curved-layer printing of 3D FRC parts. First, a novel multi-axis curved-layer slicing method based on deformed space mapping is proposed. This approach ensures that the sliced curved layers are as parallel as possible to the intended fiber orientations, improving the alignment between the printing process and fiber direction. Next, a vector field-driven printing path planning method for each curved layer is developed, which guarantees that the generated printing paths conform to the specified fiber orientations while also ensuring continuous material deposition. Additionally, a new algorithm for generating support structures tailored to curved layers is proposed, preventing material collapse during the printing process. The effectiveness of the proposed slicing method, path planning, and support structure generation are validated through extensive experiments and simulations, demonstrating their potential to significantly improve the performance and versatility of FRC additive manufacturing. Full article

In 3D printing, the layered structure often results in artifacts. This effect becomes stronger for surfaces with a lower ramp angle. This effect can be mitigated by manufacturing parts with non-planar layers that fit the parts’ surface geometry. Using the open-source slicing software PrusaSlicer. an algorithm was developed to modify the slicer’s input and output data in a way that fits parts with low ramp angle surfaces. To achieve consistent part quality, all layers were modified to be printed in a non-planar way. The test results indicate that the proposed methods can significantly reduce surface roughness. Although the algorithm works well for parts with a flat base and vertical walls, it would need to be highly adapted to work for different part geometries. Additionally, compared to other algorithms used in Curved-Layer Fused Deposition Modeling (CLFDM), the changed layer structure introduces a changed visual appearance of parts. Full article

Laser metal deposition is a branch of additive manufacturing that offers advantages over traditional manufacturing techniques for forming overhanging thin-walled metal parts. Previously, helical paths that were suitable for manufacturing such parts were not only limited to stacking material on a flat surface but were also fixed to the model boundaries. In order to solve these two problems to meet more complex process requirements, a non-planar helical path generation method is proposed for laser metal deposition. The method is based on the characteristics of the additive manufacturing process planning flow, which first slices the model using curved surfaces, then offsets the contours on the sliced layering, and finally generates continuous helical paths according to the contracted or expanded contours. In order to verify the feasibility of the method, hollow blades are formed on cylindrical surfaces following the planned paths. The results show that the proposed method is not only capable of assisting the laser metal deposition process to fabricate thin-walled structures on non-planar surfaces but also capable of freely adjusting the contour dimension. Full article

This paper presents an adaptive slicing method for Hermite non-planar tessellated surfaces models to improve the geometric accuracy of Rapid Prototyping (RP). Based on the bending characteristics of Hermite curved triangles, a slicing method for a complete Hermite surface model, including the grouping, the construction of the topological relationships, and the calculation of the intersection contours, was employed. The adaptive layering method considering the normal vector at the vertexes of the Hermite curved triangles was employed to grain the variable thickness of all layers of the Hermite surface model. The classical Stanford bunny model illustrates the significant improvement in the accuracy of the proposed method compared to the traditional method. Full article

At present, aircraft radome coating cleaning mainly relies on manual and chemical methods. In view of this situation, this study presents a trajectory planning method based on a three-dimensional (3D) surface point cloud for a laser-enabled coating cleaning robot. An automated trajectory planning scheme is proposed to utilize 3D laser scanning to acquire point cloud data and avoid the dependence on traditional teaching–playback paradigms. A principal component analysis (PCA) algorithm incorporating additional principal direction determination for point cloud alignment is introduced to facilitate subsequent point cloud segmentation. The algorithm can adjust the coordinate system and align with the desired point cloud segmentation direction efficiently and conveniently. After preprocessing and coordinate system adjustment of the point cloud, a projection-based point cloud segmentation technique is proposed, enabling the slicing division of the point cloud model and extraction of cleaning target positions from each slice. Subsequently, the normal vectors of the cleaning positions are estimated, and trajectory points are biased along these vectors to determine the end effector’s orientation. Finally, B-spline curve fitting and layered smooth connection methods are employed to generate the cleaning path. Experimental results demonstrate that the proposed method offers efficient and precise trajectory planning for the aircraft radar radome coating laser cleaning and avoids the need for a prior teaching process so it could enhance the automation level in coating cleaning tasks. Full article

The process of the additive manufacturing (AM) of carbon-fiber-reinforced polymer (CFRP) parts based on the process of fused deposition modeling (FDM) has seen considerable research in recent years, which amplifies the importance of adapted slicing and pathplanning methods. In particular, load-oriented techniques are of high interest when employing carbon fiber materials, as classical methods, such as tape-laying and laminating, struggle with highly curved and complex geometries and require the costly production of molds. While there have been some promising propositions in this field, most have restricted themselves to a planar slicing approach, which severely limits the ability to place the fibers along stress paths. In this paper, a nonplanar slicing approach is presented that utilizes principal stress directions to construct optimized nonplanar constituting layers on which pathplanning can be carried out. These layers are oriented such that the effect of the weak interlayer adhesion is minimized. Support material is adaptively generated to enable the use of arbitrary part geometry. Furthermore, a continuous pathplanning method and post-processor are applied to yield manufacturing instructions. The approach is verified for its viability of application through experimental investigation on a multi-axis robotic 3D printer. This constitutes an important step in allowing the fabrication of CFRP parts to further utilize the possibilities of additive manufacturing. Full article

In multi-axis fused deposition modeling (FDM) printing systems, support-free curved layer fabrication is realized by continuous transition of the printer nozzle orientation. However, the ability to print 3D models with complex geometric (e.g., high overhang) and topological (e.g., high genus) features is often restricted by various manufacturability constraints inherent to a curved layer design process. The crux in a multi-axis printing process planning pipeline is to design feasible curved layers and their tool paths that will satisfy both the support-free condition and other manufacturability constraints (e.g., collision-free). In this paper, we propose a volumetric curved layer decomposition method that strives to not only minimize (if not prevent) collision-inducing local shape features of layers, but also enable adaptive layer thickness to comply with a new volumetric error-based surface quality criterion. Our method computes an optimal Radial Basis Functions (RBF) field to modify the fabrication sequence field, from which, the iso-surface layers are extracted to design the corresponding multi-axis printing tool paths. A method to fine-tune variable nozzle orientations on each resulting curved layer is then proposed to overcome possible collisions in high-genus geometries. To validate the concept and exhibit its potential, several support-free fabrication experiments and comparisons with the conventional geodesic field-based slicing are presented, and the results give a preliminary confirmation of the feasibility and advantages of the proposed method. Full article

Automatic identification of short axis slice levels in cardiac magnetic resonance imaging (MRI) is important in efficient and precise diagnosis of cardiac disease based on the geometry of the left ventricle. We developed a combined model of convolutional neural network (CNN) and recurrent neural network (RNN) that takes a series of short axis slices as input and predicts a series of slice levels as output. Each slice image was labeled as one of the following five classes: out-of-apical, apical, mid, basal, and out-of-basal levels. A variety of multi-class classification models were evaluated. When compared with the CNN-alone models, the cascaded CNN-RNN models resulted in higher mean F1-score and accuracy. In our implementation and testing of four different baseline networks with different combinations of RNN modules, MobileNet as the feature extractor cascaded with a two-layer long short-term memory (LSTM) network produced the highest scores in four of the seven evaluation metrics, i.e., five F1-scores, area under the curve (AUC), and accuracy. Our study indicates that the cascaded CNN-RNN models are superior to the CNN-alone models for the classification of short axis slice levels in cardiac cine MR images. Full article

Thin-layer convective drying of plantain banana was performed at four different temperatures from 50 to 80 °C, with slice thicknesses from 2 to 8 mm. The drying curves, fitted to seven different semi-empirical mathematical models, were successfully used to fit experimental data (${\mathrm{R}}^2$ 0.72–0.99). The diffusion approach had better applicability in envisaging the moisture ratio at any time during the drying process, with the maximum correlation value (${\mathrm{R}}^2$ 0.99) and minimum value of $x^2$ (2$.5\times {10}^{-5}\mathrm{to}1.5\times {10}^{-4})$ and RMSE (5.0 $\times {10}^{-3}\mathrm{to}1.2\times {10}^{-2})$. The $D_{eff}$, $h_m$, and $E_a$ values were calculated on the basis of the experimental data, and overall ranged from $1.11\times {10}^{-10}$ to $1.79\times {10}^{-9}$ m² s⁻¹, $3.17\times {10}^{-8}$ to 2.20 $\times {10}^{-7}$ m s⁻¹ and 13.70 to 18.23 kJ mol⁻¹, respectively. The process energy consumption varied from 23.3 to 121.4 kWh kg⁻¹. The correlation study showed that the drying temperature had a close correlation with $h_m$ value and sample hardness. A significant (p < 0.05) increase in hardness of dried plantain banana was observed at 80 °C compared to the other temperatures. Additionally, the sample hardness and process energy consumption were more positively correlated with the thickness of the samples. Full article

To realize automatic wire arc additive manufacturing (WAAM) of a large aviation die with a complex gradient structure, a new contour-parallel path generation strategy was proposed and practically applied. First, the planar curve was defined as a vertical slice of a higher-dimensional surface and a partial differential equation describing boundary evolution was derived to calculate the surface. The improved Finite Element Method (FEM) and Finite Difference Method (FDM) were used to solve this partial differential equation. Second, a cross section of a large aviation die was used to test the path-generation algorithms. The results show that FEM has a faster solving speed than FDM under the same solving accuracy because the solving domain of FEM mesh was greatly reduced and the boundary mesh could be refined. Third, the die was divided into three layers: base layer, transition layer (Fe-based material) and strengthening layer (Co-based material) according to the difference of the temperature and stress field, and corresponding WAAM process parameters has been discussed. The optimum welding parameters are obtained as follows: voltage is 28 V, wire feeding speed is 8000 mm/min and welding speed is 450 mm/min. Finally, the path generation strategy was practically applied to the remanufacture of the large aircraft landing gear die with a three-layer structure. The application test on aircraft landing gear dies justified the effectiveness of the algorithms and strategy proposed in this paper, which significantly improved the efficiency of the WAAM process and the service life of large aviation dies with complex gradient structures. The microstructure of the fusion zone shows that the base metal and welding material can be fully integrated into the welding process. Full article

Functionally graded materials (FGM) have received extensive attention in recent years due to their excellent mechanical properties. In this research, the theoretical process of calculating the propagation characteristics of Lamb waves in FGM sandwich plates is deduced by combining the FGM volume fraction curve and Legendre polynomial series expansion method. In this proposed method, the FGM plate does not have to be sliced into multiple layers. Numerical results are given in detail, and the Lamb wave dispersion curves are extracted. For comparison, the Lamb wave dispersion curve of the sliced layer model for the FGM sandwich plate is obtained by the global matrix method. Meanwhile, the FGM sandwich plate was subjected to finite element simulation, also based on the layered-plate model. The acoustic characteristics detection experiment was performed by simulation through a defocusing measurement. Thus, the Lamb wave dispersion curves were obtained by V(f, z) analysis. Finally, the influence of the change in the gradient function on the Lamb wave dispersion curves will be discussed. Full article

The Standard Template Library (STL) file is the most common data format for the description of an additive manufacturing (AM) geometric model, but it has some disadvantages, such as large errors of the geometric model description, the easy loss of topology information, data duplication, large file sizes, and so on. Aiming at these problems, a direct slicing algorithm based on a Standard for the Exchange of Product Model Data (STEP) model was proposed. For the parts composed of basic types of surfaces such as boundary curves, spherical surfaces and cylindrical surfaces, the traditional geometric method was used to calculate the intersection. For the parts with complex surfaces, the three-dimensional models were described based on Non-Uniform Rational B-Spline (NURBS) surfaces. The NURBS surfaces were layered using a discrete tracking algorithm, the tracking starting point was determined, the intersection line between the tangent plane and each NURBS sub-surface was obtained, and the closed layer contour was formed. Finally, the slicing simulations and printing experiments of solid parts were carried out using the direct slicing algorithm based on the STEP model. It was shown that the dimensional accuracy and surface quality of the printed parts from this algorithm had been significantly improved. Full article