Shape Characterization Research Articles

Enhanced mesoscale and macroscale parameters for accurate 3D shape characterization

This paper investigates the limitations of widely used 3D Wadell's roundness and true sphericity metrics for characterizing granular materials. It introduces two improved indices: the mesoscale interlocking index and the radial distance factor. The study involves diverse particles scanned using optical interferometry and X-ray computed tomography. A mean curvature method is employed for surface segmentation, and a computationally efficient 3D mesh simplification procedure is proposed. Python scripts are developed for surface segmentation, mesh simplification, and 3D shape characterization. The new indices are validated by assessing angular and rounded 3D particles and changes in the shape of aggregates through the Los Angeles abrasion test. The results demonstrate the ability of the proposed indices to differentiate visually similar particles with identical Wadell's roundness and true sphericity, showcasing their effectiveness in accurately describing particle shape. The statistical distribution of 3D particle curvatures offers an alternative method for understanding particles' shape and abrasion.

3D shape and size characterization of micron-sized coal particle with XRCT and SH

The shape and size of solid fuel particles are significant parameters in combustion process, and have attracted broad attentions in simulation and experiment studies. In this study, X-ray computer tomography (XRCT) and spherical harmonics (SH) were introduced and combined to conduct accurate morphology characterization of micron-sized coal powder in three dimensions (3D) with purpose of providing references for morphology characterization, size measurement, and modeling of solid fuel particles. The shape parameters including volume, surface area, sphericity index, specific surface area, enlongation index, and flatness index, and the size parameters including diameter of volume equivalent sphere, three principal dimensions, and sieve size, were obtained with SH expansion. Validation on standard geometries demonstrated the accuracy of the method under the SH degree N ≥ 20, with errors for volume, surface area, and sieve size within 1 %, 3 %, and 2.5 %, respectively. These parameters were analyzed statistically to reveal the morphological characteristics of micron-sized coal powder. The Zingg diagram indicated that nearly 60 % of the coal powder particles are nearly spherical. Comparison among 3D size, 2D size, and sieve size implied that characterizing with 2D size may overestimate the fineness of coal powder. Although there was a variance in the particle size distribution, it was demonstrated that there exists a strong linear relationship between the 3D size, 2D size, and sieve size. The method could serve as a functional auxiliary tool for fuel particle research.

Influence of difference in deformation modulus between asphalt concrete core and transition layer on core behavior and difference threshold determination

Asphalt concrete core is the main impermeable structure of asphalt concrete core dams (ACCDs). The deformation modulus difference between the core and transition layer is directly related to the core behavior, especially the arch effect. This paper mainly studies the threshold of the deformation modulus difference between the transition and asphalt concrete core and the core arc safety. A database of 54 ACCD cases from domestic and abroad is collected. The thickness and material characteristics of the transition and core are statistically analyzed, and the range of deformation modulus ratio η between them is determined. A numerical model is established, and the impact of different interface models on the core behavior is compared and analyzed. By comparing measured data and numerical calculation results, an appropriate interface simulation method is selected to study the influence of deformation modulus ratio η on the core performance. On this basis, the threshold ηmax is determined using a cusp catastrophe model. A new valley shape characterization parameter δ is proposed, and the arch safety area under different δ is studied based on ηmax for reference in ACCDs design and construction. Finally, engineering measures to enhance the arch safety of the core are discussed.

Mechanical pretreatment of typical agricultural biomass on shape characterization and NO emissions during combustion

The impact of mechanical pretreatment of corn straw (CS), pea straw (PS), and wheat straw (WS), on shape characterization and NO emissions during combustion were investigated in this research. Particle size ranges were obtained and characterized their shape factors using Image J correction. The thermal properties and NO emissions of the different-sized particles were investigated by TG-MS and fixed-bed reactor. Compared with CS and PS, WS is more likely to break into smaller particles due to its moderate strength. Amine-N completely disappeared after pyrolysis, whereas pyrrolic-N and pyridinic-N were the main N functionalities in char-N. During the volatile burning stage, the maximum peak of NO concentration was 270, 354 and 311 ppm for CS, PS and WS, respectively. NO was detected at a steady level during the semicoke combustion stage, and the duration increased with particle size. The NO concentration decreased sharply in a short duration during the fixed carbon combustion stage.

Characterization and depth detection of internal delamination defects in CFRP based on line laser scanning infrared thermography

In this article, the shape characterization and depth detection of delamination defects in carbon fiber reinforced polymer (CFRP) sandwich are achieved by detecting delamination defects through line laser infrared thermography. In terms of defect shape characterization, the temperature matrix difference imaging method is proposed to characterize the shape of internal defects, and the defect shape distortion is corrected by the principal component analysis algorithm to optimize the effect of defect shape characterization. In terms of defect depth detection, this study constructs a dataset of cooling curves for defects of different depths and builds an encoder–decoder convolutional neural network structure based on the attention mechanism, which is centered on a streamlined encoder–decoder structure as well as a highly integrated attention gate, which is capable of accelerating the extraction of features and reducing the number of iterations of the algorithm. The results demonstrate that we achieved an average classification accuracy of 88.9% for defects with a depth gradient difference of 0.2 mm, attaining the highest classification accuracy of 95% for a single defect. Furthermore, our approach outperformed several classical classifiers by achieving superior accuracy with fewer iterations. The experimental results demonstrate that the line laser scanning inspection method is effective in the task of detecting internal defects in CFRP materials.

Introducing the Video In Situ Snowfall Sensor (VISSS)

Abstract. The open-source Video In Situ Snowfall Sensor (VISSS) is introduced as a novel instrument for the characterization of particle shape and size in snowfall. The VISSS consists of two cameras with LED backlights and telecentric lenses that allow accurate sizing and combine a large observation volume with relatively high pixel resolution and a design that limits wind disturbance. VISSS data products include various particle properties such as maximum extent, cross-sectional area, perimeter, complexity, and sedimentation velocity. Initial analysis shows that the VISSS provides robust statistics based on up to 10 000 unique particle observations per minute. Comparison of the VISSS with the collocated PIP (Precipitation Imaging Package) and Parsivel instruments at Hyytiälä, Finland, shows excellent agreement with the Parsivel but reveals some differences for the PIP that are likely related to PIP data processing and limitations of the PIP with respect to observing smaller particles. The open-source nature of the VISSS hardware plans, data acquisition software, and data processing libraries invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.

Characterization of Particle Shape with an Improved 3D Light Scattering Sensor (3D-LSS) for Aerosols

To characterize fine particulate products in industrial gas–solid processes, insights into the particle properties are accessible via various measurement techniques. For micron particles, online imaging techniques offer a fast and reliable assessment of their size and shape. However, for the shape analysis of submicron particles, only offline techniques, such as SEM and TEM imaging, are available. In this work, an online sensor system based on the principle of elastic light scattering of particles in the gas phase is developed to measure the shape factor of non-spherical particles in the size range of 500 nm to 5 µm. Single aerosol particles are guided through a monochromatic circularly polarized laser light beam by an aerodynamic focusing nozzle, which was developed based on the CFD simulation of the flow and particle movement. The intensity of the scattered light is measured at several discrete positions in the azimuthal direction around the particles. An algorithm computes the sphericity of the particles based on the distribution of the intensity signals. The sensor construction, data processing and analysis are described. Model aerosols with particles of different shapes are investigated to test the developed sensor and show its performance in the determination of the sphericity distribution of particles.

Physical, morphological, and mechanical properties of raw and steamed cashew nuts (Anacardium occidentale L.)

ABSTRACT Cashew (Anacardium occidentale L.) plays an important role in global agriculture, particularly in commercializing the cashew kernel. Nevertheless, kernel extraction poses a challenge due to the nut variable shape and size. Characterizing the properties of the cashew nut becomes a pivotal input for the advancement of processes and machinery. This work presents a comprehensive characterization of raw and steamed cashew nuts from Puerto Carreño, Colombia, with focus on morphological, physical, and mechanical properties. Cashew nut samples from different farms in the region were collected. Morphological properties, including size and shape, were analyzed by measuring length, width, and thickness for 100 nuts from each supplier, both in their raw state and after steaming. Geometric diameter and sphericity were calculated, and qualitative shape characterization was performed. Physical properties, such as mass, bulk density, true density, and moisture content, were also measured. Angle of repose and coefficient of friction were determined to assess flow and frictional behavior. Additionally, compressive mechanical properties were evaluated through uniaxial compression testing along three orientations. The findings indicated significant differences in morphological and mechanical properties between raw and steamed cashew nuts. Conversely, the results demonstrated that cashew nuts from Vichada region exhibit a noteworthy degree of dispersion and larger dimensions than those from Asia and Africa, comparable to those from Brazil. The findings guide machinery design in the cashew industry and offer insights into the physical, morphological, and mechanical properties of cashew nuts in Puerto Carreño, contributing to understanding and enabling quality assessment, processing optimization, and product development.

Assessment of microplastic abundance and impact on recreational beaches along the western Algerian coastline

Microplastics (MPs) have attracted global attention due to their ubiquity and potential impact on the environment. However, MPs are poorly documented along the Algerian coast. To address this data gap, this study investigated the presence of MPs on three recreational beaches in western Algeria (Stidia, Sablettes and Mersat El Hadjaj). The mean MP abundance for the three beaches was estimated at 55.47 ± 48.01 items/m2. Characterization of MPs shape revealed that fragments (79.81 %) were dominant, followed by pellets (10.58 %), filaments (5.57 %), and foam (3.85 %). An assessment of the Microplastic Pollution Index, the Pellet Pollution Index, and the Coefficient of Microplastic Impact, revealed the presence of very low to moderate levels of MPs on the beaches. This presence was associated with minimal beach pollution and moderate overall impact. The sector analysis approach results confirm that there are two different magnitudes of MPs presence that can negatively impact on the environmental quality of the studied beaches.

Octagonal lattice-based triangulated shape descriptor engaging second-order derivatives supplementing image retrieval

Erstwhile shape description schemes lack primarily in establishing trade-offs with accuracy and computational load. Accordingly, a lightweight shape descriptor offering precise definition and compaction of high-frequency features is contributed in this paper using a simple geometrical shape for localization and shape characterization. Initially, the input image is octagonally tessellated and triangularly decomposed into sub-regions whose side-wise differences are evaluated and subjected to second-order differentiation to produce three high-frequency values representing triangle corners. The resultant is processed by the law of sines to yield localized shape features exhibiting congruence and is reiterated on the residual regions, followed by a novel octal encoding scheme encompassing maximal variations in the localized regions. The resulting features are globally fabricated into shape histograms in a non-overlapping manner representing the shape vector. This scheme validated on widely popular benchmark shape datasets demonstrates superior retrieval and recognition accuracies greater than 93% which is lacking in its competitors.

Automated characterization and monitoring of material shape using Riemannian geometry

Shape affects both the physical and chemical properties of a material. Characterizing the roughness, convexity, and general geometry of a material can yield information on its catalytic efficiency, solubility, elasticity, porosity, and overall effectiveness in the application of interest. However, material shape can be defined in a multitude of conflicting ways where different aspects of a material’s geometry are emphasized over others, leading to bespoke measures of shape that are not easily generalizable. In this paper, we explore the use of Riemannian geometry in the analysis of shape, and show that a Riemannian geometric framework for shape analysis is generalizable, computationally scalable, and can be directly integrated into common data analysis methods. In this framework, material shapes are abstracted as points on a Riemannian manifold. This information can be used to construct statistical moments (e.g., means, variances) and perform tasks such as dimensionality reduction and statistical process control. We provide a practical introduction to the mathematics of shape analysis through Riemannian geometry and illustrate its application on a manufactured/mined granular material dataset provided by Covia Corp. We show that the Riemannian framework can be used to automatically extract and quantify the shape of granular materials in a statistically rigorous manner.

Deep-learning versus greyscale segmentation of voids in X-ray computed tomography images of filament-wound composites

Filament-wound composites (FWC) are prone to high void contents, with large and complex-shape voids. It is critical to characterise these voids accurately to understand their effect on part strength. The characterization depends on the accuracy of the analysis technique, for example X-ray computed tomography and the subsequent void segmentation. This paper compares conventional greyscale thresholding to deep-learning (DL) based segmentation. The processing steps for both techniques are discussed. The greyscale thresholding contains segmentation errors due to the simple one-parameter algorithm and the pre-processing operations required for segmentation. This reduces the accuracy of void characterisation. The DL-based segmentation is found to be more accurate for characterisation of void size, shape, and location. The processing-time and system requirements are discussed, helping to determine the suitable segmentation technique based on desired results.

Automated rapid artefact surface area measurement from imagery with computer vision

Automated surface area measurements have been of interest to archaeologists since digital imagery began allowing researchers to remotely collect artefact metrics. We present a method of automatically measuring 2D surface area from artefact planform images employing computer vision in Python. The Python script, provided as a .py file in supplementary data, creates boundaries around regions of relatively homogeneous pixels (artefacts) in the image. These bounded regions are called contours. A count of the number of pixels within each contour provides a surface area in pixels. A circular reference object provides a conversion factor for the contours, as well as a point of reference for geometric accuracy of outputs.
 Measurements of 2D artefact surface area can be used in combination with measurements of length, width, thickness, and mass, or in some cases, replace such measurements. As presented, this technique provides utility to archaeology with applications to new documentation of artefacts, archived artefact images containing a scale, as well as landscape geoarchaeology and sedimentary contexts. Limitations of this type of surface area measurement include the requirement of the image background being of a solid colour heavily contrasting the artefacts being measured. Effectively, the background requirement limits deployment supporting collection of rapid field measurements from in-situ surface scatters without modification to the script or manipulation of the artefacts. Analytical applications utilizing this technique include studies of relative artefact abundance, shape and size class characterizations in artefact scatters, and redistribution of artefacts by geomorphological processes.

Insecticidal efficacy of two candidate diatomaceous earth powders against the lesser grain borer, Rhyzopertha dominica (F.) (Coleoptera: Bostrichidae)

The insecticidal efficacy of amorphous silica powders 1 and 2 with different physical and elemental compositions were evaluated against the lesser grain borer, Rhyzopertha dominica (F.) using concrete arenas. The particle diameter and shape characterization (circularity, elongation, aspect ratio, and convexity) of the two silica powders were performed using Morphology G3 instrument. The particle diameter and shape characteristics (circularity and elongation) of silica powder 1 were significantly higher compared to silica powder 2. However, these factors did not influence the efficacy of silica powders against R. dominica. Silica powder 2 showed higher mortality at lower application rates and shorter exposure times compared to silica powder 1. The efficacy of silica powder 2 was due to its higher oil adsorption capability, low pH, and high silicon dioxide content compared to silica powder 1. Adult progeny production was inversely related to the powder application rate. Silica powder 2 was more effective on R. dominica in inhibiting adult progeny production at 0.25 g/m2. Lower percentages of insect-damaged kernels and grain weight loss were observed at lower application rates (0.10–0.20 g/m2) following exposure to silica powder 2 compared to silica powder 1, which required higher application rates (2.0–3.5 g/m2). Overall, silica powder 2 demonstrated superior efficacy against R. dominica in terms of mortality, progeny production, insect-damaged kernels, and grain weight loss.

Shape characterization and depth recognition of metal cracks based on laser infrared thermography and machine learning

Due to the operation of modern industrial equipment under high pressure, high speed and high load conditions, cracks inevitably appear on the surfaces of metal components. Crack detection on metal surfaces is increasingly becoming a focal point in the field of non-destructive testing. In order to locate and quantitatively analyze surface cracks on metallic materials as early as possible to reduce the cost in industrial production, this paper achieves rapid localization of surface cracks by establishing a line laser thermography scanning system. An unsupervised edge detection algorithm of principal component analysis combined with K-means (PK) is proposed to achieve accurate crack edge detection by combining with a point-pulse laser thermography system. The accuracy of the PK algorithm is confirmed by comparison with other edge detection algorithms. After that, accurate identification of crack depth is achieved by selecting temperature features through particle swarm optimization algorithm and optimizing hyper-parameters of support vector machine and k-nearest neighbor through Bayesian search method. The experiments show that the best recall and accuracy of PK for crack detection are 82.9% and 86.2%, respectively; Combining the particle swarm optimization algorithm with the BS method, the mean square error of SVR and KNN for crack depth recognition is 0.092 and 0.089, respectively.

Combined imaging and chromatic confocal microscopy technique to characterize size and shape of ensembles of cuboidal particles

The presence of needle- and plate-like particles has detrimental consequences on their downstream processing in the fine chemicals sector. Therefore, the ability to accurately characterize the particle size and shape is essential to quantify and predict their impact on the product processability. Nonetheless, tools to characterize both the size and shape of ensembles of cuboidal particles are seldom available. The overarching goal of this work is to provide a fast and accurate offline size and shape characterization tool. To this aim, we have designed and experimentally validated a combined imaging and chromatic confocal microscopy technique. We propose two modes of operation: one that facilitates the accurate 3D reconstruction of particles; and the other that facilitates their rapid characterization. We validate the performance of our technique using a commercial technique. We show that our technique can accurately characterize thousands of particles, making it a valuable addition to existing process analytical technology.

Integration of orthogonal Fourier profilometry with blood flow mapping for 3D fingertip biometric authentication

We present herein the combination of Fourier transform profilometry based orthogonal projection with a laser speckle imaging method to provide three-dimensional (3D) fingertip blood flow imaging and shape reconstruction for biometric authentication purposes. In the proposed approach, orthogonal sinusoidal grating patterns with a fixed spatial frequency emanating from a digital projector first illuminate the surface of a person's finger. Then, the projector is blocked and a laser source illuminates the finger. The projector and laser work alternately such that only one source illuminates the finger at a given time allowing no illumination crosstalk between the two devices. The deformed grating patterns and the diffuse backscattered laser light, respectively, are recorded on the same CCD camera. In the computer, the 3D shape of the finger is reconstructed simultaneously with a blood flow map of the finger. Finally, the images are merged yielding a dynamic flow topography visualization of the person's finger. This dual-modality strategy utilizes the strengths of each imaging method in a complementary way, thus enabling simple 3D shape recovery and characterization of a specific user, and increasing the specificity of authentication security. The method is experimentally validated on both human and fake fingers through VanderLugt optical correlation framework. Overall, the results demonstrate the utility of our proposed approach for biometric authentication. We envision that our optoelectronic setup could be potentially integrated into a wide variety security platform.

Experimental investigation of mixing railway ballast grains with different form using large-scale direct shear box apparatus

This paper investigates the effect of grain form on the mechanical behaviour of a railway ballast aggregate using large-scale shear box tests. The speciality of the research is that – contrarily to most of the experimental studies, where the shape content of the assemblies are not manipulated – here, the grains were carefully sorted and the ballast samples were assembled with the desired size and form content. A new sorting method was developed for laboratory-scale samples to separate compact and non-compact (flat, elongated or bladed) grains. This method utilises the combination of standard bar sieves and shape index calliper. The geometries of the grains in the resultant assemblies were thoroughly investigated using modern 3D imaging-based shape characterisation methods. With the adequate parameter thresholds, good correspondence was found between the standard industrial and the modern imaging-based methods, which allows the virtual reproduction of the introduced laboratory sorting technique. Three types of andesite railway ballast assemblies were tested experimentally: a fully compact, a fully non-compact and a mixed one. The mixed assembly had the highest internal friction angle which proves that it can be beneficial to mix grains with different types form. The amount of broken mass and the failure modes of the grains were also recorded, and it was found that the non-compact assembly had the highest rate of breakage. The results of the study also serve as a good basis for the calibration of numerical models.

Analysis of stretch distribution of high compliant elastomers within folded lumen vessels

Abstract Elastomer based high compliant balloons containing strain sensing element(s) (SEs) is currently under development intended for in-vivo biomechanical diagnostics of vessels. It could potentially reveal local lumen features based on patterns derived from the sensing elements. A Finite Element based, simulation study in COMSOL® (v5.6) focusses on in-vivo inflation behavior of an elastomeric balloon being equipped with SE, whose compliance is ideally magnitudes higher than the surrounding tissue in an idealized 2D setup. We hypothesized the vessel’s inner wall as a closed convexconcave 4-fold structure correlated to surface structures found in urethrae and parameterized the fold depth. A set of SEs consisted of one SE over the inner surface of balloon while the other over the outer wall. Out of the three adjacent placed sets, The first set was closer to the tissue lumen while the third set the farthest. We assessed the stretch of balloon over its inner circumference through SEs. At conformal contact with the tissue wall, The first SE shows a higher value, while the third element undergoes the least stretch within the three sensing elements. The SE’s over the outer circumference show the exact opposite relation. The differences between the sensing elements over the inner and outer circumferences show a correlation with the curvature of the tissue it conforms to. With use of balloon that was 10x thicker (10μm vs 1μm), around 10x larger stretch differences were captured suggesting possibility to use less sensitive measuring system. For the ideal situations performed, the curvature and depth information may be comprehended by observing differences between inner and outer SEs while thicker balloons prove to be more useful to generate differences that are easier to capture in practical situations. This semiquantitative study suggests that simulation studies are powerful tools to obtain new analytical techniques for shape characterization.

Design and evaluation of a portable frequency comb-referenced laser heterodyne radiometer

In this paper, we present the design of a laser heterodyne radiometry instrument that combines, for the first time, frequency comb calibration and a remarkably high level of portability. A design that can, therefore, be more than capable of addressing the current need for accurate ground-based greenhouse gases monitoring in urban areas and other emission hot spots. Indeed, the compact, battery-powered system allows the acquisition of atmospheric spectral characterizations, at any location, without restrictions. As its most prominent feature, the system is equipped with an electro-optic frequency comb reference that provides a set of calibration ticks from which an accurate characterization of the absorption line shape can be obtained. Besides this, the spectrometer has been designed to promptly switch between traditional operation and wavelength modulation, so the performance of future inversion models may benefit greatly by this complementary data. The system has been tested in different locations in the Madrid region (Spain), where measurements have been carried out under a wide variety of conditions. Here, a set of highly representative results is presented clearly illustrating the capabilities of the developed system.