Identification Of Geometric Features Research Articles

Deciphering Double-Walled Corrugated Board Geometry Using Image Analysis and Genetic Algorithms.

Corrugated board, widely used in the packing industry, is a recyclable and durable material. Its strength and cushioning, influenced by geometry, environmental conditions like humidity and temperature, and paper quality, make it versatile. Double-walled (or five-ply) corrugated board, comprising two flutes and three liners, enhances these properties. This study introduces a novel approach to analyze five-layered corrugated board, extending a previously published algorithm for single-walled boards. Our method focuses on measuring the layer and overall board thickness, flute height, and center lines of each layer. Through the integration of image processing and genetic algorithms, the research successfully developed an algorithm for precise geometric feature identification of double-walled boards. Images were recorded using a special device with a sophisticated camera and image sensor for detailed corrugated board cross-sections. Demonstrating high accuracy, the method only faced limitations with very deformed or damaged samples. This research contributes significantly to quality control in the packaging industry and paves the way for further automated material analysis using advanced machine learning and image sensors. It emphasizes the importance of sample quality and suggests areas for algorithm refinement in order to enhance robustness and accuracy.

Identification of Geometric Features of the Corrugated Board Using Images and Genetic Algorithm.

The corrugated board is a versatile and durable material that is widely used in the packaging industry. Its unique structure provides strength and cushioning, while its recyclability and bio-degradability make it an environmentally friendly option. The strength of the corrugated board depends on many factors, including the type of individual papers on flat and corrugated layers, the geometry of the flute, temperature, humidity, etc. This paper presents a new approach to the analysis of the geometric features of corrugated boards. The experimental set used in the work and the created software are characterized by high reliability and precision of measurement thanks to the use of an identification procedure based on image analysis and a genetic algorithm. In the applied procedure, the thickness of each layer, corrugated cardboard thickness, flute height and center line are calculated. In most cases, the proposed algorithm successfully approximated these parameters.

Neighbor Reweighted Local Centroid for Geometric Feature Identification.

Identifying geometric features from sampled surfaces is a significant and fundamental task. The existing curvature-based methods that can identify ridge and valley features are generally sensitive to noise. Without requiring high-order differential operators, most statistics-based methods sacrifice certain extents of the feature descriptive powers in exchange for robustness. However, neither of these types of methods can treat the surface boundary features simultaneously. In this paper, we propose a novel neighbor reweighted local centroid (NRLC) computational algorithm to identify geometric features for point cloud models. It constructs a feature descriptor for the considered point via decomposing each of its neighboring vectors into two orthogonal directions. A neighboring vector starts from the considered point and ends with the corresponding neighbor. The decomposed neighboring vectors are then accumulated with different weights to generate the NRLC. With the defined NRLC, we design a probability set for each candidate feature point so that the convex, concave and surface boundary points can be recognized concurrently. In addition, we introduce a pair of feature operators, including assimilation and dissimilation, to further strengthen the identified geometric features. Finally, we test NRLC on a large body of point cloud models derived from different data sources. Several groups of the comparison experiments are conducted, and the results verify the validity and efficiency of our NRLC method.

Identification of Geometrical Features of Cell Surface Responsible for Cancer Aggressiveness: Machine Learning Analysis of Atomic Force Microscopy Images of Human Colorectal Epithelial Cells.

It has been recently demonstrated that atomic force microscopy (AFM) allows for the rather precise identification of malignancy in bladder and cervical cells. Furthermore, an example of human colorectal epithelial cells imaged in AFM Ringing mode has demonstrated the ability to distinguish cells with varying cancer aggressiveness with the help of machine learning (ML). The previously used ML methods analyzed the entire cell image. The problem with such an approach is the lack of information about which features of the cell surface are associated with a high degree of aggressiveness of the cells. Here we suggest a machine-learning approach to overcome this problem. Our approach identifies specific geometrical regions on the cell surface that are critical for classifying cells as highly or lowly aggressive. Such localization gives a path to colocalize the newly identified features with possible clustering of specific molecules identified via standard bio-fluorescence imaging. The biological interpretation of the obtained information is discussed.

Identifying Balls Feature in a Large-Scale Laser Point Cloud of a Coal Mining Environment by a Multiscale Dynamic Graph Convolution Neural Network.

In the process of coal mining, a certain amount of gas will be produced. Environmental perception is very important to realize intelligent and unmanned coal mine production and operation and to reduce the accident rate of gas explosion and other disasters. The identification of geometric features of the coal mine working face is the main part of the environmental perception of the working face. In this study, we identify geometric features in a large-scale coal mine working face point cloud (we take the ball as an example) so as to provide a method for the environmental perception of the coal mine working face. On the basis of previous research, we upgrade the dynamic graph convolution neural network (DGCNN) for directly processing point clouds from two aspects: extracting local features and global features of point clouds. First, a multiscale dynamic graph convolution neural network (MS-DGCNN) is proposed, and the combination of max-pooling and average-pooling is used as the symmetry function. Second, we use MS-DGCNN to learn the features of a variety of geometric point clouds in the point cloud data set we make and then look for the ball in the large-scale point cloud of the coal mining working face. Finally, we compare the performance of MS-DGCNN with that of other deep neural networks directly processing point clouds. This study enables MS-DGCNN to obtain more powerful feature expression ability and enhance the generalization of the model. In addition, this study provides a solid foundation for the geometric feature identification of MS-DGCNN in the environmental perception of the coal mine working face and creates a precedent for the application of MS-DGCNN in the field of energy. At the same time, this study makes a beneficial exploration for the development of a transparent coal mine working face.

A CAD-based design for manufacturing method for casted components

The paper proposes a method to couple manufacturing knowledge in the context of casting with 3D CAD modelling and design. The approach allows formalizing tacit into explicit design knowledge, for aiding engineers during the development of metal casted components. It is based on three main pillars: (i) identification of geometrical features (3D CAD features) and parameters that can cause an issue during the manufacturing process, (ii) definition of a numerical threshold for feature parameters that guarantee the feasibility of the casting process, and (iii) representation of design rules within a CAD system to support product design review. The method is considered the starting point for future developing a software tool (CAD tool plug-in), here just presented. Two case studies are reported with the aim to show the advantages of the proposed method and tool in the identification of manufacturing issues early in the product development process. Results highlight how the CAD-based tool is a useful assistant to avoid design problems related to the metal casting process.

An Automatic Mesh Generator for Hexahedral Element Mesh Using an Improved Grid-Based Method

An automatic mesh generator AUTOMESH-3D is developed by the authors in this paper. The generator can adaptively produce hexahedral element meshes with an improved inside-out grid-based method. This method involves seven steps: STL model construction, geometric feature identification, cubic grid structure generation, jagged core mesh generation, surface-gap filling, boundary matching and quality improvement. Adaptive refinement, local refinement and boundary matching techniques are employed to generate eligible elements according to the model’s features and users’ requirements. Several examples of hexahedral element mesh generation with AUTOMESH-3D are given, and the effectiveness and robustness of the mesh generator are demonstrated.

A knowledge base model for complex forging die machining

Recent evolutions on forging process induce more complex shape on forging die. These evolutions, combined with High Speed Machining (HSM) process of forging die lead to important increase in time for machining preparation. In this context, an original approach for generating machining process based on machining knowledge is proposed in this paper. The core of this approach is to decompose a CAD model of complex forging die in geometrical features. Technological data and topological relations are aggregated to a geometrical feature in order to create machining features. Technological data, such as material, surface roughness and form tolerance are defined during forging process and dies design. These data are used to choose cutting tools and machining strategies. Topological relations define relative positions between the surfaces of the die CAD model. After machining features identification cutting tools and machining strategies currently used in HSM of forging die, are associated to them in order to generate machining sequences. A machining process model is proposed to formalize the links between information imbedded in the machining features and the parameters of cutting tools and machining strategies. At last machining sequences are grouped and ordered to generate the complete die machining process. In this paper the identification of geometrical features is detailed. Geometrical features identification is based on machining knowledge formalization which is translated in the generation of maps from STL models. A map based on the contact area between cutting tools and die shape gives basic geometrical features which are connected or not according to the continuity maps. The proposed approach is illustrated by an application on an industrial study case which was accomplished as part of collaboration.

Research on Intelligent MCAD/CAPP/CAFD Integrated System Modeling

Feature extraction and expression of three-dimensional MCAD entity model is independent of platform. According to STEP, this paper discusses for a unified MCAD / CAPP / CAFD integrated system to establish the program model and study the key technologies. The STEP neutral file that contains the model is taken to decide the design features of the part. The identification of geometric features and geometric characteristics of features to the mapping process, rule-based reasoning and case-based reasoning with the way things feature-based process planning techniques in the process of reasoning problems. UML object-oriented technology and methods such as functional analysis of the system and structural design, the initial mode of integrated system functional model and structure is established

A Framework for Geometric Analysis of Vascular Structures: Application to Cerebral Aneurysms

There is well-documented evidence that vascular geometry has a major impact in blood flow dynamics and consequently in the development of vascular diseases, like atherosclerosis and cerebral aneurysmal disease. The study of vascular geometry and the identification of geometric features associated with a specific pathological condition can therefore shed light into the mechanisms involved in the pathogenesis and progression of the disease. Although the development of medical imaging technologies is providing increasing amounts of data on the three-dimensional morphology of the in vivo vasculature, robust and objective tools for quantitative analysis of vascular geometry are still lacking. In this paper, we present a framework for the geometric analysis of vascular structures, in particular for the quantification of the geometric relationships between the elements of a vascular network based on the definition of centerlines. The framework is founded upon solid computational geometry criteria, which confer robustness of the analysis with respect to the high variability of in vivo vascular geometry. The techniques presented are readily available as part of the VMTK, an open source framework for image segmentation, geometric characterization, mesh generation and computational hemodynamics specifically developed for the analysis of vascular structures. As part of the Aneurisk project, we present the application of the present framework to the characterization of the geometric relationships between cerebral aneurysms and their parent vasculature.

Cataloguing the geometry of the human coronary arteries: A potential tool for predicting risk of coronary artery disease

Background The non-uniform distribution of atherosclerosis in the human vasculature suggests that local fluid dynamics or wall mechanics may be involved in atherogenesis. Thus certain aspects of vascular geometry, which mediates both fluid dynamics and wall mechanics, might be risk factors for coronary atherosclerosis. Cataloguing the geometry of normal human coronary arteries and its variability is a first step toward identifying specific geometric features that increase vascular susceptibility to the disease. Methods Images of angiographically normal coronary arteries, including 32 left anterior descending (LAD) and 35 right coronary arteries (RCA), were acquired by clinical biplane cineangiography from 52 patients. The vessel axes in end diastole were reconstructed and geometric parameters that included measures of curvature, torsion and tortuosity were quantified for the proximal, middle and distal segments of the arteries. Results Statistical analysis shows that (1) in the LAD, curvature, torsion and tortuosity are generally highest in the distal portion, (2) in the RCA, these parameters are smallest in the middle segment, (3) the LAD exhibits significant higher torsion than the RCA ( P < 0.005), and (4) > 80% of the variability of coronary arterial geometry can be expressed in terms of two factors, one dominated by the curvature measures and tortuosity, and the other emphasizing the torsion parameters. Conclusions This study has comprehensively documented the normal arterial geometry of the LAD and RCA in end diastole. This information may be used to guide the identification of geometric features that might be atherogenic risk factors.

Development of Parametric Mesh Morphing Technique

We developed the parametric mesh morphing technique, which can be used to change the shape of existing analytical mesh models, and generate a mesh model for a new shape by morphing the mesh model for the previous one. Therefore, our method is suitable to use for analysis during the early stage of design because it reduces the amount of time required to generate analytical mesh models. Compared with conventional methods, our method preserves the geometric features of the original mesh in the morphed mesh because of geometric feature identification and parametric mesh morphing. Geometric feature identification technique can classify outside surface of mesh as plane, quadric (cylinder, cone and sphere) and free-surface. Shape of mesh can be transformed in maintaining the identified geometry feature by using developed parametric mesh morphing technique. We demonstrated the effectiveness of our method by applying it to models of a bracket and a connection rod.