Geometric Identification 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.

Characterizations of Kerr-de Sitter in arbitrary dimension from null infinity.

The Kerr and Kerr-de Sitter metrics share remarkable local geometric properties in four dimensions. Gibbons et al. found a generalization of the Kerr-de Sitter metric to higher dimensions, to which the local characterization above cannot be applied. One viable approach to characterize this family is to understand the behaviour of these metrics at future null infinity. We review Friedrich's and Fefferman-Graham formalisms to discuss the asymptotic initial value problem of ([Formula: see text])-vacuum spacetimes in arbitrary dimensions and study their properties: geometric identification and conformal equivalence of data, Killing initial data and conformal equivalence of boundary conformal Killing vectors (CKV). These results are used to review a recent characterization of Kerr-de Sitter in terms of its asymptotic data, namely conformal flatness at [Formula: see text] together with a canonical TT tensor constructed from specific CKV at [Formula: see text]. Allowing for arbitrary CKV defines the (larger) Kerr-de Sitter-like class. All these metrics can be obtained explicitly as limits or analytic extensions of Kerr-de Sitter. The Kerr-de Sitter-like class is also characterized by the property of being Kerr-Schild and fulfilling a certain falloff condition. In addition, in five dimensions, this class corresponds to all algebraically special metrics with non-degenerate optical matrix. This article is part of a discussion meeting issue 'At the interface of asymptotics, conformal methods and analysis in general relativity'.

Initial Clustering of a Point Cloud via K-means: A Mathematical Framework for Geometric Primitives Identification

Initial Clustering of a Point Cloud via K-means: A Mathematical Framework for Geometric Primitives Identification

Recreating horizontal alignments of existing railways with a hybrid analytic and harmony search algorithm

After operating a long time, existing railway tracks gradually deviate from their initially designed alignments due to the wheel-track interaction, thereby compromising operational performance and safety. Therefore, existing railway alignments should be recreated through periodic maintenance to enhance train operations and guarantee operational safety. Currently, most methods in this field first identify tangent alignment sections and then curves based on measured points along the railway. Afterward, these identified geometric components are optimized to fit the actual alignment. However, for existing railways that are fairly circuitous, the above strategy is limited since the curves’ geometric characteristics dominate those of tangents in recreating alignments. Thus, the precision of geometric identification is reduced and the recreated alignment may significantly deviate from the actual railway. To solve this problem, a three-stage optimization method is proposed. At the first stage, a dynamic threshold method is developed to flexibly identify the geometric attributions of all measured points based on the existing railway. For the second stage, an adaptive analytic method is proposed which combines two geometric recreation approaches that are devised for curves and tangents to determine a preliminarily recreated horizontal alignment. Moreover, to enhance the recreation accuracy of local alignment sections, a harmony search algorithm is customized as the third stage to further optimize the recreated alignment. Finally, the effectiveness of the proposed method is verified through detailed data and sensitivity analyses applied to a real-world case.

Improving accuracy reconstruction of parts through a capability study: A methodology for X-ray Computed Tomography Robotic Cell

Aeronautical performance is enhanced by increasingly complex and lightweight composite parts. The improvement of their manufacturing must ensure the quality measured by Non-Destructive Testing. To evaluate the health of aeronautical parts that are a few meters long, we built a high dimensional robotic cell including two industrial robots positioned on 5-meter-tracks equipped with X-ray Computed Tomography devices. Our main objective is to reconstruct the interior of the parts and detect any anomaly that may have occurred during life cycle or manufacturing (such as porosity or inclusions). In this way, the objective is to present a methodology, firstly, to evaluate the raw process capability and secondly, to assess the improved process capability. The raw process capability uses geometrical identification and we demonstrate that without proper identification, some defects can remain hidden. Three strategies are then developed. The first one involves improving the robot behavior model, which takes into account a thermo-geometrical model, including backlash compensation. Due to possible repositioning problems and to ensure the correct knowledge of the source with respect to the detector, the second strategy involves a real-time test phantom located on the detector named the comb prototype. Finally, a large-sized steel balls phantom is designed to allow a calibration in the full workspace of the robots. This phantom is also used to accurately determine the axis of the rotary horizontal-axis support on which the large parts to be controlled are fixed. We demonstrate that such an architecture, for reconstructing a perfect sphere, leads to an initial mean radius error of 0.05 mm which we improve to 0.016-0.017 mm with our methodology.

Geometric identification of dynamic objects on image sequences

Geometric identification of dynamic objects on image sequences

ZooMS confirms geometric morphometrics species identification of ancient sheep and goat.

Geometric morphometrics can effectively distinguish isolated third lower molars of present-day sheep and goat, but its applicability to archaeological specimens has yet to be established. Using a modern reference collection of 743 sheep and goats and a two-dimensional landmark-based geometric morphometric (GMM) protocol, this study aimed to morphometrically identify 109 archaeological specimens, used as case studies, dating from the Late Neolithic to the modern period/era. These morphometric identifications were then compared to molecular identifications via collagen peptide mass fingerprinting, known as Zooarcheology by Mass Spectrometry (ZooMS). ZooMS confirmed the morphometric identifications for 104 specimens, with the five misidentified specimens all morphometrically identified as goat. Modern sheep and goats have larger teeth and distinct shapes compared to their archaeological counterparts, suggesting strong differences between archaeological and modern specimens potentially linked with recent breed improvement or geographical origin of the specimens. In addition, for both species, some of the archaeological dental morphologies do not match with any of our modern references. This study validates the applicability of geometric morphometrics for identifying isolated archaeological sheep and goat teeth. It represents a stepping stone for future, non-destructive, bioarchaeological studies of the two species.

Structural Assessment of a Water Reservoir Masonry Vaulted Roof from the Nineteenth Century

This article presents an experimental and numerical study on the structural safety of an old buried water reservoir built in the second half of the nineteenth century. The main goal of the study was to assess the feasibility of changing the reservoir roof from a garden with limited access to an outdoor exhibition or concert area, with an increase in service loads. The reservoir comprises a set of cylindrical vaults, built with rubble masonry and lime mortar, supported by a set of stone masonry arches and columns. Because no original design elements exist, the following methodology was followed: (1) a structural survey was first carried out for geometrical and material identification and for assessment of anomalies; (2) core samples were extracted from the vaulted roof to characterize the material properties of the rubble masonry and lime mortar; (3) a three-dimensional (3D) nonlinear finite element (FE) model of the structure was developed and used for structural safety verification; and (4) an in-situ static load test was carried out, in which the roof was subjected to a uniformly distributed load for assessment of its structural response and to validate the results from the numerical model. Overall, this study showed that the vaulted roof of the water reservoir has sufficient load capacity for its future use. Moreover, this work illustrates the application of a comprehensive methodology to assess the structural safety of a historical and very particular type of construction.

Hybrid iteration and optimization-based three-dimensional reconstruction for space non-cooperative targets with monocular vision and sparse lidar fusion

The measurement of reference pose and geometric size for space non-cooperative targets is an essential premise of on-orbit servicing. In this paper, a three-dimensional (3-D) reconstruction and autonomous geometric parameter identification method is proposed. An autonomous region segmentation algorithm with monocular vision and sparse lidar fusion is introduced, which realizes the multi-source match of rich features in images and scale information in point clouds. In order to densify the sparse point cloud, a multi-view fusion model is constructed with multi-objective optimization, which realizes the complementation of 3-D information. Further, a 3-D reconstruction method with hybrid iteration and optimization is proposed. Unlike the method with random sampling, the proposed method can obtain the optimal solution while reducing the influence of outliers. In other to verify the effectiveness of the proposed method, a complete set of simulation and experimental systems with modular and scalable software is built. The results show that the accuracy and stability are improved by no less than 27.88% and 38.99%, respectively. Besides, the inference speed is enhanced by at least one time. The proposed method can accurately and quickly identify the geometric parameters of targets with a cubical body, which will be used for motion estimation. Moreover, it can be extended to targets with a polyhedral body.

Numerical Method for Geometrical Feature Extraction and Identification of Patient-Specific Aorta Models in Pediatric Congenital Heart Disease

An algorithm providing information on the key geometric features of an aorta extracted from multi-slice computed tomography images is proposed. Using the numerical method, the aorta’s geometric characteristics, such as vessel cross-sectional areas and diameters, as well as distances between arteries, can be determined. This step is crucial for training the meta-model necessary to construct an expert system with a significantly reduced volume of data and for identifying key relationships between diagnoses and geometric and hydrodynamic features. This methodology is expected to be part of an innovative decision-making software product for clinical implementation. Based on clinical and anamnestic data as well as calculations, the software will provide the shunt parameters (in particular, its diameter) and installation position to ensure regular blood flow.

MANAGEMENT PROPERTIES AND PROCEDURES IN THE INFORMATION MODEL OF THE HISTORIC BUILDING HBIM ON BUILDING FACADES

Abstract. Historic Building Information Modelling (HBIM) has become the ideal tool for professionals studying building conservation and restoration. The BIM model allows one to identify structural deformations following three-dimensionally resolved models. In addition, this methodology is specifically designed to register parametric construction models, as an information manager by adding semantic components to the model, including information on the different transformations of the historical artefact, and allowing continuous progress in the building's life cycle. In this work, graphic information and data related to conservation projects are compiled that allow a management of all the documentation integrated in the same Project, from all the disciplinary fields related to the heritage conservation and rehabilitation process. Specifically, from the geometric identification and the restoration documents, it is intended to develop an experimental application where both terrestrial laser scanning records and the use of image processing routines can allow automatic operations to monitor alterations in façades, in order to subsequent control by conservation experts. For this, a building with certain characteristics has been chosen, such as the Miguel de Mañara palace in the city of Seville. A building that has a main façade of 43.30 metres in length by 6.75 metres in height and, in which its façade rotates in an angled guideline to adapt to the urban planning of the time. The analysis is based on the applicability of two segmentation algorithms and the construction of the multilayer enclosure model where the different stratigraphies of the results obtained are exposed.

Acoustic feature based geometric defect identification in wire arc additive manufacturing

ABSTRACT In additive manufacturing of metals, numerous techniques have been employed to sense print defects. Among these, acoustic-based sensing has the advantage of low cost and shows the most potential to identify both external and internal defects as an in-situ monitoring system. Using acoustic signals, researchers have broadly investigated non-machine learning and machine learning-based approaches to identify defects like balling, micro defects, lack of fusion pores, keyhole pores, cracks, and porosity. While most of these works have shown promising results for laser-based AM systems, few have explored how acoustic signals can be used effectively for Wire Arc Additive Manufacturing (WAAM) defect detection. This paper proposes a methodology to construct machine learning (ML)-based models on identifying geometrically defective bead segments using acoustic signals during the WAAM process. Geometrically defective bead segment or geometric defect is a defect that causes voids in the final printed part due to incomplete fusion between two non-uniform overlapping bead segments. Such a defect is currently not explored in the literature. The proposed methodology uses a novel dataset labeling approach to identify good and bad bead segments based on an optimal threshold of the range of mean curvature. Furthermore, the methodology targets defective bead segments based on acoustic feature inputs like Principal Components (PC) or Mel Frequency Cepstral Coefficients (MFCC). To understand the resulting performance of the defect identification models constructed based on the proposed methodology, experiments are performed and tested on a variety of ML models (KNN, SVM, RF, NN, and CNN) based on the Inconel 718 material. The results show that the combinatorics of two acoustic input features and five ML models can be able to identify geometrically defective segments accurately with F1 score that ranges from 80% to 85%.

Automatic extraction method of force chain information and its application in the flow photoelastic experiment of granular matter

The force chain is the core of the multi-scale analysis of granular matter. Accurately extracting the force chain information among particles is of great significance to the study of particle mechanics and geological hazards caused by particle flow. However, in the photoelastic experiment, the precise identification of the branching points of force chains has not been effectively realized. Therefore, this study proposes an automatic extraction method of force chain key information. First, based on the Hough transform and the Euclidean distance, a particle geometric information identification model is established and geometric information such as particle circle center coordinates, radius, contact point location, and contact angle is extracted. Then, a particle contact force information identification model is established following the color gradient mean square method. The model realizes the rapid calibration and extraction of a large number of particle media contact force information. Next, combined with the force chain composition criterion and its quasilinear feature, an automatic extraction method of force chain information is established, which solves the problem of the accurate identification of the force chain branch points. Finally, in the photoelastic experiment of ore drawing from a single drawpoint, the automatic extraction method of force chain information is verified. The results show that the macroscopic distribution of force chains during ore drawing from a single drawpoint is left–right symmetrical. Strong force chains are mostly located on the two sides of the model but in small numbers and they mainly develop vertically. Additionally, the ends are mostly in a combination of Y and inverted Y shapes, while the middle is mostly quasilinear. Weak force chains are abundant and mostly distributed in the middle of the model, and develop in different directions. The proposed extraction method accurately extracts the force chain network from the photoelastic experiment images and dynamically characterizes the force chains of granular matter, which has significant advantages in particle geometry information extraction, force chain branch point discrimination, force chain retrieval, and force chain distribution and its azimuthal characterization. The results provide a scientific basis for studying the macroscopic and microscopic mechanical parameters of granular matter.

LOS Signal Identification Based on Common Chord Intersection Point Position Deviation from MS in Target Localization

Target localization has been a popular research topic in recent years since it is the basis of all kinds of location-based applications. For GNSS-denied urban or indoor environments, the localization method based on time-of-arrival (TOA) is one of the most popular localization methods due to its high accuracy and simplicity. However, the Non-line-of-sight (NLOS) error is the major cause that degrades the accuracy of the TOA-based localization method. Identifying whether a received signal at a base station (BS) is due to a line-of-sight (LOS) transmission or NLOS is the key to TOA-based localization methods. In the popular LOS signal identification methods, compared with statistic signal methods and machine learning methods, the geometric constraint method has the advantages of simplicity and without requiring priori knowledge of signals and large amounts of training datasets. In this paper, we propose a geometric constraint two-step LOS signal identification method based on common chord intersection point position deviation from mobile stations (MS). In the first step, all BSs are divided into multiple BS combinations with every three BSs, the TOA distance error of each BS combination is estimated based on common chord intersection point position deviation from MS, the BS combinations whose TOA distance error satisfy Gaussian distribution are roughly identified as LOS BS combination and enter the second step, the other BS combinations are discarded as NLOS BS combination. In the second step, based on mutual distance threshold and discrimination result matrix, common chord intersection points of LOS BS combination, and corresponding LOS BS combinations are identified. The BSs of LOS BS combinations are identified as LOS BS and the signals received at LOS BS are identified as LOS signal ultimately. Compared with the other two geometric constraint methods, the proposed algorithm has better identification accuracy, and the setting of the identification threshold value has a theoretical basis, which facilitates the application of the proposed algorithm.

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.

Application of Digital Twin in Medium-Voltage Overhead Distribution Network Inspection

The paper is devoted to the digital transformation of the core engineering maintenance processes involved in medium-voltage overhead distribution network infrastructure inspection. The study presents an analysis of digital twin utilization for inspection of the infrastructure of medium-voltage overhead distribution networks. At present, the infrastructure monitoring process is still manual, and its automatization is a challenging task due to the large distances between and small dimensions of the elements involved. The proposed digital twin is employed for 3D infrastructure modeling and complex analysis based on photogrammetry and aerial scanning data processing methods. The paper describes practical use cases for these data-driven methods for specific infrastructure management processes, e.g., scheduled inspection processes, including geometrical parameter measurements and visual infrastructure element defect identification, unplanned inspection processes (state of emergency, post-storm, etc.), and vegetation management processes. The proposed method allows operations to be performed remotely without physical presence in the field. At the same time, data-driven solutions provide objective results and potential automation via machine learning algorithms, which are more profitable economically. As the proposed method allows processes to be performed remotely, these data-driven solutions offer cost-effective results for automation. The digital twin concept is applied to the Latvian medium-voltage overhead distribution network with the support of the Latvian distribution system operator “Sadales tīkls” JSC.

Virtual volume correlation of lattice structures: From volumetric data to geometrical and dimensional defects identification

Additively manufactured lattice structures are widely studied in the literature for their advantageous properties. Manufacturing steps introduce defects that are often classified amongst geometry and dimension, surface quality, and porosity defects. Because these defects drastically affect lattice structures’ behaviour, the manufactured parts might lack their required properties. Assessments of these properties are mainly performed by finite-element analysis. This approach primarily relies on meshed X-ray computed tomography (XCT) measurements or meshed computer-aided design (CAD) models and less on parametric models where defects can be easily implemented. Although the former is the most representative of the real part geometry, measured geometrical features are implicit in this analysis and cannot be identified in a direct parametric approach. This paper proposes a virtual volume correlation (V2C) methodology to extract geometrical and dimensional defects of laser powder bed fusion lattice structures. The V2C methodology only relies on volumetric measurement and does not require additional XCT post-reconstruction steps. Such a methodology combines strut and node displacement fields that are decomposed as a sum of chosen parametric elementary displacement fields. V2C is first validated on generated volumes where known defects are introduced, and its ability to relocate these introduced defects is evaluated. Relocation errors are found to be lower than half a voxel. Then, V2C is applied to manufactured lattice structures that differ in position and orientation. Results show the dependence of strut typology, whether vertical or inclined strut, on the resulting geometrical and dimensional defects. V2C also shows the orientation and the position effect of the lattice structure’s geometrical and dimensional defect within the building chamber where lattice nodes’ displacement amplitudes are about 30 μm. The conclusion outlines the parametric identification of geometrical and dimensional defects that can be easily implemented for further finite element models.

Geometric mistuning identification and finite element model updating methods for bladed disks

Dynamic analysis results of a blisk are sensitive to inevitable geometrical mistuning, and the high-fidelity finite element model (FEM) of real blisk is of paramount importance to accurately predict structural dynamics. However, it is still challenging to accurately identify geometric mistuning and obtain a high-fidelity FEM due to the complexity and stochastic nature of geometric mistuning. In this paper, considering various types of geometrical mistuning, both a novel geometric mistuning identification and automatic finite element model updating (FEMU) method for real blisk using measured point cloud data are presented to reduce the discrepancies between the real blisk and the numerical model. The geometric surface of the real blisk is digitized to the point cloud data by a non-contact optical measurement technique. Three implementation steps of the method are introduced: 1) morphological feature detection from measured point cloud data of blisk; 2) surface nodes space mapping of FEM using isoparametric transformation; and 3) mesh shape optimization for FEMU. Eventually, two experimental cases are investigated: a simplified blisk and an industry blisk. The robustness of pre-processing algorithms for geometrical feature detection with measurement noise is also conducted. Results show that the blades with small mistuning, large deformations, and damages in the tip and leading edges are accurately identified and updated using the proposed method. Compared with measured natural frequencies for updated FEM, the maximum error is less than 3.38‰.

A Machine-Learning-Based Approach to Critical Geometrical Feature Identification and Segmentation in Additive Manufacturing

Additive manufacturing (AM) processes offer a good opportunity to manufacture three- dimensional objects using various materials. However, many of the processes, notably laser Powder bed fusion, face limitations in manufacturing specific geometrical features due to their physical constraints, such as the thermal conductivity of the surrounding medium, the internal stresses, and the warpage or weight of the part being manufactured. This work investigates the opportunity to use machine learning algorithms in order to identify hard-to-manufacture geometrical features. The segmentation of these features from the main body of the part permits the application of different manufacturing strategies to improve the overall manufacturability. After selecting features that are particularly problematic during laser powder bed fusion using stainless steel, an algorithm is trained using simple geometries, which permits the identification of hard-to-manufacture features on new parts with a success rate of 88%, showing the potential of this approach.

A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms.

Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region’s cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage.