Tomography of Materials and Structures最新文献

英文中文

Development of AI crack segmentation models for additive manufacturing

Tomography of Materials and Structures

Pub Date : 2025-02-01 DOI: 10.1016/j.tmater.2025.100053

Tebogo Ledwaba , Christine Steenkamp , Agnieszka Chmielewska-Wysocka , Bartlomiej Wysocki , Anton du Plessis

The use of X-ray computed tomography (XCT) has seen significant growth over a broad range of disciplines including biology, earth science, engineering, and many more. It is now increasingly used in additive manufacturing (AM) since its benefits are being appreciated more widely. This is due to the method being non-destructive and comprehensive, providing external and internal information of tested parts. Data processing and segmentation of XCT data is important to get as much information as possible so that a clear picture of features can be obtained and analyzed. Porosity analysis has been the most successful and widely used XCT analysis type in all fields so far, partly due to simple manual segmentation methods such as the Otsu global threshold. However, segmentation of small and narrow features such as cracks are challenging with conventional thresholding methods. Since automated conventional methods fail, manual segmentation is often used but this can be subjective, tedious, and prone to segmentation errors. The present work employs neural networks, specifically the U-Net architecture and thoroughly investigates possible solutions to a robust crack segmentation model. Intensity scale calibration, bias training weights and data augmentations were investigated in detail to find the best possible performance of trained models, when employed on new data. The results demonstrate the performance and improvement gained by each of the above factors, as well as the successful AI segmentation for various additively manufactured sample types with different cracks. This method enables clear visualization and presentation of cracks, as well as their quantification. The model strives toward a generic crack segmentation model for all AM parts that could be used directly by others. This generalizability of the model is discussed together with its limitations.

{"title":"Development of AI crack segmentation models for additive manufacturing","authors":"Tebogo Ledwaba , Christine Steenkamp , Agnieszka Chmielewska-Wysocka , Bartlomiej Wysocki , Anton du Plessis","doi":"10.1016/j.tmater.2025.100053","DOIUrl":"10.1016/j.tmater.2025.100053","url":null,"abstract":"<div><div>The use of X-ray computed tomography (XCT) has seen significant growth over a broad range of disciplines including biology, earth science, engineering, and many more. It is now increasingly used in additive manufacturing (AM) since its benefits are being appreciated more widely. This is due to the method being non-destructive and comprehensive, providing external and internal information of tested parts. Data processing and segmentation of XCT data is important to get as much information as possible so that a clear picture of features can be obtained and analyzed. Porosity analysis has been the most successful and widely used XCT analysis type in all fields so far, partly due to simple manual segmentation methods such as the Otsu global threshold. However, segmentation of small and narrow features such as cracks are challenging with conventional thresholding methods. Since automated conventional methods fail, manual segmentation is often used but this can be subjective, tedious, and prone to segmentation errors. The present work employs neural networks, specifically the U-Net architecture and thoroughly investigates possible solutions to a robust crack segmentation model. Intensity scale calibration, bias training weights and data augmentations were investigated in detail to find the best possible performance of trained models, when employed on new data. The results demonstrate the performance and improvement gained by each of the above factors, as well as the successful AI segmentation for various additively manufactured sample types with different cracks. This method enables clear visualization and presentation of cracks, as well as their quantification. The model strives toward a generic crack segmentation model for all AM parts that could be used directly by others. This generalizability of the model is discussed together with its limitations.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"7 ","pages":"Article 100053"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contrast-enhancing staining agents for ex vivo contrast-enhanced computed tomography: A review

Tomography of Materials and Structures

Pub Date : 2025-01-31 DOI: 10.1016/j.tmater.2025.100052

T. Balcaen , S. Vangrunderbeeck , W.M. De Borggraeve , G. Kerckhofs

Ex vivo microCT imaging has emerged as a powerful tool for 3D histology of biological tissues, offering significant advantages in terms of spatial resolution, simplicity of protocols and acquisition speed. Among the various techniques available, contrast-enhanced computed tomography (CECT) is particularly favored for its ability to simultaneously visualize soft and mineralized tissue types through the use of contrast agents (CAs), making it suitable for laboratory-based microCT devices. This review focuses on contrast-enhancing staining agents (CESAs), a subclass of CAs, which enrich the X-ray attenuating atom content in soft tissues through interactions. Within this review, CESAs are categorized based on their chemical composition into organic, mixed (i.e. heavy metal and organic ligand) and inorganic compounds, each with specific properties and applications. Despite the growing interest and numerous studies on CESAs, the selection process often relies on trial-and-error, anecdotal knowledge, or commercial availability. This review aims to enhance understanding of the chemical interactions and distribution patterns of CESAs within biological tissues, by discussing a selection of studies grouping observations by tissues and organs, to gain a better understanding of consistent affinity patterns. The findings highlight the complexity and accompanying challenges of predicting CESA distribution. This review will provide a foundation for both intelligent CESA selection and design, tailored to specific research needs as well as a guide for the application expert in choosing relevant literature for designing their experiments.

{"title":"Contrast-enhancing staining agents for ex vivo contrast-enhanced computed tomography: A review","authors":"T. Balcaen , S. Vangrunderbeeck , W.M. De Borggraeve , G. Kerckhofs","doi":"10.1016/j.tmater.2025.100052","DOIUrl":"10.1016/j.tmater.2025.100052","url":null,"abstract":"<div><div><em>Ex vivo</em> microCT imaging has emerged as a powerful tool for 3D histology of biological tissues, offering significant advantages in terms of spatial resolution, simplicity of protocols and acquisition speed. Among the various techniques available, contrast-enhanced computed tomography (CECT) is particularly favored for its ability to simultaneously visualize soft and mineralized tissue types through the use of contrast agents (CAs), making it suitable for laboratory-based microCT devices. This review focuses on contrast-enhancing staining agents (CESAs), a subclass of CAs, which enrich the X-ray attenuating atom content in soft tissues through interactions. Within this review, CESAs are categorized based on their chemical composition into organic, mixed (<em>i.e.</em> heavy metal and organic ligand) and inorganic compounds, each with specific properties and applications. Despite the growing interest and numerous studies on CESAs, the selection process often relies on trial-and-error, anecdotal knowledge, or commercial availability. This review aims to enhance understanding of the chemical interactions and distribution patterns of CESAs within biological tissues, by discussing a selection of studies grouping observations by tissues and organs, to gain a better understanding of consistent affinity patterns. The findings highlight the complexity and accompanying challenges of predicting CESA distribution. This review will provide a foundation for both intelligent CESA selection and design, tailored to specific research needs as well as a guide for the application expert in choosing relevant literature for designing their experiments.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"7 ","pages":"Article 100052"},"PeriodicalIF":0.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143212849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visualizing pulp fibers using X-ray tomography: Enhancing the contrast by labeling with iron oxide nanoparticles and the use of immersion oil

Tomography of Materials and Structures

Pub Date : 2025-01-28 DOI: 10.1016/j.tmater.2025.100051

Anderson T.V. Veiga , Elisa S. Ferreira , James Drummond , Lewis Mason , Samuel N.M. Brown , André Phillion , D. Mark Martinez , Emily D. Cranston

In this study, we present a protocol to visualize the architecture of tracer fibers in paper using X-ray tomography. We prepared tracer fibers by depositing iron oxide nanoparticles on the surface of select papermaking fibers, through a multicycle labeling technique that achieved 14 wt% of iron. Labeled and unlabeled fibers on their own, as well as laboratory-formed paper containing a small fraction of the tracer fibers, were imaged in air and after immersion in a non-polar oil. We found that labeled fibers could be segmented from the background through simple binarization when in the immersed state whereas segmentation failed when the samples were imaged in air. We propose that the oil served as a mask, created through compositional and density matching of the unlabeled fibers to the saturated void volume. This new labeling and immersion protocol opens avenues to enhance the contrast of tracers for improved characterization of cellulosic materials via X-ray tomographic imaging in an approach that does not require advanced image processing methods for segmentation.

{"title":"Visualizing pulp fibers using X-ray tomography: Enhancing the contrast by labeling with iron oxide nanoparticles and the use of immersion oil","authors":"Anderson T.V. Veiga , Elisa S. Ferreira , James Drummond , Lewis Mason , Samuel N.M. Brown , André Phillion , D. Mark Martinez , Emily D. Cranston","doi":"10.1016/j.tmater.2025.100051","DOIUrl":"10.1016/j.tmater.2025.100051","url":null,"abstract":"<div><div>In this study, we present a protocol to visualize the architecture of tracer fibers in paper using X-ray tomography. We prepared tracer fibers by depositing iron oxide nanoparticles on the surface of select papermaking fibers, through a multicycle labeling technique that achieved 14 wt% of iron. Labeled and unlabeled fibers on their own, as well as laboratory-formed paper containing a small fraction of the tracer fibers, were imaged in air and after immersion in a non-polar oil. We found that labeled fibers could be segmented from the background through simple binarization when in the immersed state whereas segmentation failed when the samples were imaged in air. We propose that the oil served as a mask, created through compositional and density matching of the unlabeled fibers to the saturated void volume. This new labeling and immersion protocol opens avenues to enhance the contrast of tracers for improved characterization of cellulosic materials via X-ray tomographic imaging in an approach that does not require advanced image processing methods for segmentation.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"7 ","pages":"Article 100051"},"PeriodicalIF":0.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

3D mineral quantification of particulate materials with rare earth mineral inclusions: Achieving sub-voxel resolution by considering the partial volume and blurring effect

Tomography of Materials and Structures

Pub Date : 2025-01-26 DOI: 10.1016/j.tmater.2025.100050

Shuvam Gupta , Vivian Moutinho , Jose R.A. Godinho , Bradley M. Guy , Jens Gutzmer

This study documents a significant enhancement to the recently introduced Mounted Single Particle Characterization and Mineralogical Analyses (MSPaCMAn) workflow for particulate samples by X-ray computed tomography analyses. This enhancement is used to quantify the abundance of small grains of rare earth minerals within particulate samples of iron ore. In the studied samples, rare earth minerals are typically present as minute grains. The small size creates challenges for X-ray computed tomography due to the well-known partial volume and blurring effects. This effect is particularly pronounced when the sizes of grains start to approach the sizes of voxels. The enhanced MSPaCMAn workflow incorporates new steps to improve the reliability of mineral characterization by simultaneously analyzing the grey values and geometrical properties of rare earth mineral grains and their host minerals. The refined workflow also enables the comprehensive characterization of particle surfaces. The results of the MSPaCMAn were validated by scanning electron microscopy-based automated mineralogy and X-ray powder diffraction data. The study is a step towards accurate and reproducible mineralogical quantification of particulate processing samples using X-ray 3D imaging.

{"title":"3D mineral quantification of particulate materials with rare earth mineral inclusions: Achieving sub-voxel resolution by considering the partial volume and blurring effect","authors":"Shuvam Gupta , Vivian Moutinho , Jose R.A. Godinho , Bradley M. Guy , Jens Gutzmer","doi":"10.1016/j.tmater.2025.100050","DOIUrl":"10.1016/j.tmater.2025.100050","url":null,"abstract":"<div><div>This study documents a significant enhancement to the recently introduced Mounted Single Particle Characterization and Mineralogical Analyses (MSPaCMAn) workflow for particulate samples by X-ray computed tomography analyses. This enhancement is used to quantify the abundance of small grains of rare earth minerals within particulate samples of iron ore. In the studied samples, rare earth minerals are typically present as minute grains. The small size creates challenges for X-ray computed tomography due to the well-known partial volume and blurring effects. This effect is particularly pronounced when the sizes of grains start to approach the sizes of voxels. The enhanced MSPaCMAn workflow incorporates new steps to improve the reliability of mineral characterization by simultaneously analyzing the grey values and geometrical properties of rare earth mineral grains and their host minerals. The refined workflow also enables the comprehensive characterization of particle surfaces. The results of the MSPaCMAn were validated by scanning electron microscopy-based automated mineralogy and X-ray powder diffraction data. The study is a step towards accurate and reproducible mineralogical quantification of particulate processing samples using X-ray 3D imaging.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"7 ","pages":"Article 100050"},"PeriodicalIF":0.0,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Geo-SegNet: A contrastive learning enhanced U-net for geomaterial segmentation

Tomography of Materials and Structures

Pub Date : 2025-01-20 DOI: 10.1016/j.tmater.2025.100049

Qinyi Tian , Sara Goodhue , Hou Xiong , Laura E. Dalton

X-ray micro-computed tomography scanning and tomographic image processing is a robust method to quantify various features in geomaterials. The accuracy of the segmented results can be affected by factors including scan resolution, scanning artifacts, and human bias. To overcome these limitations, deep learning techniques are being explored to address these challenges. In the present study, a novel deep learning model called Geo-SegNet was developed to enhance segmentation accuracy over traditional U-Net models. Geo-SegNet employs contrastive learning for feature extraction by integrating this extractor as the encoder in a U-Net architecture. The model is tested using 10 feet of sandstone cores containing significant changes in porosity and pore geometries and the segmentation results are compared to common segmentation methods and U-Net. Compared to a U-Net-only model, Geo-SegNet demonstrates a 2.0 % increase in segmentation accuracy, indicating the potential of the model to improve the segmentation porosity which can also improve subsequent metrics such as permeability.

引用次数: 0

Photo-oxidation of semicrystalline polymers: Effect of stress triaxiality on ductility

Tomography of Materials and Structures

Pub Date : 2025-01-08 DOI: 10.1016/j.tmater.2025.100048

K.N. Cundiff , T.F. Morgeneyer , A.A. Benzerga

The effect of stress triaxiality on the strain-to-fracture of as-received and photo-oxidized polyamide-6 (PA-6) was investigated using mechanical testing, synchrotron X-ray tomography, and finite element analyses. Mechanical tests were conducted on cylindrical and round notched specimens, where different notch radii were used to vary the stress triaxiality. The specimens were aged by exposure to ultra-violet (UV) radiation at 60^∘, causing photo-oxidation. As-received and so-aged specimens were loaded to failure (complete loss of load carrying capacity). For both unaged and aged specimens, a higher triaxiality led to a lower strain-to-fracture. To elucidate the micromechanical damage that mediates fracture in both conditions, specimens with an intermediate notch sharpness were loaded to the peak load, unloaded, and scanned ex situ using synchrotron X-ray tomography. Damage in the unaged bar was found to occur by cavitation and was concentrated at the center of the specimen, where the triaxiality is highest. In the UV-aged bar, a network of inter-connected chemical cracks were found on the notch surface, where the triaxiality is lowest. Finite element analyses were deployed to approximate the local triaxiality at damaged regions in the unaged and UV-aged specimens using a constitutive relation for semicrystalline polymers. From these analyses, the relationship between local triaxiality and strain-to-fracture was quantified for both unaged and photo-oxidized PA-6. Both unaged and photo-oxidized PA-6 showed similar decreases in ductility with triaxiality, hinting at common ductile fracture processes.

{"title":"Photo-oxidation of semicrystalline polymers: Effect of stress triaxiality on ductility","authors":"K.N. Cundiff , T.F. Morgeneyer , A.A. Benzerga","doi":"10.1016/j.tmater.2025.100048","DOIUrl":"10.1016/j.tmater.2025.100048","url":null,"abstract":"<div><div>The effect of stress triaxiality on the strain-to-fracture of as-received and photo-oxidized polyamide-6 (PA-6) was investigated using mechanical testing, synchrotron X-ray tomography, and finite element analyses. Mechanical tests were conducted on cylindrical and round notched specimens, where different notch radii were used to vary the stress triaxiality. The specimens were aged by exposure to ultra-violet (UV) radiation at 60<sup>∘</sup>, causing photo-oxidation. As-received and so-aged specimens were loaded to failure (complete loss of load carrying capacity). For both unaged and aged specimens, a higher triaxiality led to a lower strain-to-fracture. To elucidate the micromechanical damage that mediates fracture in both conditions, specimens with an intermediate notch sharpness were loaded to the peak load, unloaded, and scanned <em>ex situ</em> using synchrotron X-ray tomography. Damage in the unaged bar was found to occur by cavitation and was concentrated at the center of the specimen, where the triaxiality is highest. In the UV-aged bar, a network of inter-connected chemical cracks were found on the notch surface, where the triaxiality is lowest. Finite element analyses were deployed to approximate the local triaxiality at damaged regions in the unaged and UV-aged specimens using a constitutive relation for semicrystalline polymers. From these analyses, the relationship between local triaxiality and strain-to-fracture was quantified for both unaged and photo-oxidized PA-6. Both unaged and photo-oxidized PA-6 showed similar decreases in ductility with triaxiality, hinting at common ductile fracture processes.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"7 ","pages":"Article 100048"},"PeriodicalIF":0.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning-supported visual analytics for high resolution X-ray inspection of metal matrix composites

Tomography of Materials and Structures

Pub Date : 2025-01-02 DOI: 10.1016/j.tmater.2024.100047

Thomas Lang , Anja Heim , Christoph Heinzl

Metal matrix composites are utilized in a multitude of applications due to their mechanical and thermodynamical properties, which are highly dependent on the microstructure. A detailed characterization is thus vital for a sound understanding of the material’s properties. X-ray computed tomography, in particular high resolution synchrotron imaging, presents a promising inspection method for this purpose. However, a high-resolution inspection of medium-sized samples produces very large volumetric datasets, which prevents a proper data analysis with commonly available tools and software. We propose a workflow for analyzing large volumetric datasets of particle-reinforced metal matrix composites, from 3D renderings of the datasets to qualitative and quantitative characterizations of the material regarding shape and spatial distribution of the contained particles. Each step in this workflow is designed to be applicable to arbitrarily large volumetric datasets. Application-dependent visualizations facilitate derived secondary information to become accessible, generating in-depth insights despite the large number of particles. The workflow is demonstrated on a large high-resolution dataset in qualitative and quantitative evaluations, whose visual representations confirm that the distribution of particles within the sample is quite homogeneous albeit the presence of minor agglomerations.

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引用次数: 0

Self-supervised resolution enhancement for anisotropic volumes in edge illumination X-ray phase contrast micro-computed tomography

Tomography of Materials and Structures

Pub Date : 2024-12-23 DOI: 10.1016/j.tmater.2024.100046

Jiayang Shi , Louisa Brown , Amir R. Zekavat , Daniël M. Pelt , Charlotte K. Hagen

X-ray phase contrast micro-computed tomography (micro-CT) can achieve higher contrast than conventional absorption-based X-ray micro-CT by utilizing refraction in addition to attenuation. In this work, we focus on a specific X-ray phase contrast technique, edge illumination (EI) micro-CT. EI uses a sample mask with transmitting apertures that split the X-ray beam into narrow beamlets, enabling detection of refraction-included intensity variations. Between the typical mask designs (circular and slit-shaped apertures), slit-shaped apertures offer practical advantages over circular ones, as they only require sample stepping in one direction, thereby reducing scanning time. However, this leads to anisotropic resolution, as the slit-shaped apertures enhances resolution only along the direction orthogonal to the slits. To address this limitation, we propose a self-supervised method that trains on high-resolution in-plane images to enhance resolution for out-of-plane images, effectively mitigating anisotropy. Our results on both simulated and real EI micro-CT datasets demonstrate the effectiveness of the proposed method.

{"title":"Self-supervised resolution enhancement for anisotropic volumes in edge illumination X-ray phase contrast micro-computed tomography","authors":"Jiayang Shi , Louisa Brown , Amir R. Zekavat , Daniël M. Pelt , Charlotte K. Hagen","doi":"10.1016/j.tmater.2024.100046","DOIUrl":"10.1016/j.tmater.2024.100046","url":null,"abstract":"<div><div>X-ray phase contrast micro-computed tomography (micro-CT) can achieve higher contrast than conventional absorption-based X-ray micro-CT by utilizing refraction in addition to attenuation. In this work, we focus on a specific X-ray phase contrast technique, edge illumination (EI) micro-CT. EI uses a sample mask with transmitting apertures that split the X-ray beam into narrow beamlets, enabling detection of refraction-included intensity variations. Between the typical mask designs (circular and slit-shaped apertures), slit-shaped apertures offer practical advantages over circular ones, as they only require sample stepping in one direction, thereby reducing scanning time. However, this leads to anisotropic resolution, as the slit-shaped apertures enhances resolution only along the direction orthogonal to the slits. To address this limitation, we propose a self-supervised method that trains on high-resolution in-plane images to enhance resolution for out-of-plane images, effectively mitigating anisotropy. Our results on both simulated and real EI micro-CT datasets demonstrate the effectiveness of the proposed method.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"7 ","pages":"Article 100046"},"PeriodicalIF":0.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental method for rubber deformation analysis using in situ X-ray tomography and digital volume correlation with FEM validation

Tomography of Materials and Structures

Pub Date : 2024-12-11 DOI: 10.1016/j.tmater.2024.100045

J. Lachambre , A. Sibellas , J. Adrien , J. Papillon , R. Bruant , G. Maurel , E. Maire

The grip and rolling resistance of car wheel tires on a road surface is strongly influenced by the mechanical properties of the rubber and especially by the deformation of this rubber during indentation by the road asperities. This paper presents the results of a simplified indentation experiment. A cylindrical block of rubber containing microstructural markers is indented by a sphere in situ inside an X-ray Computed Tomograph. The presence of markers intentionally added to the rubber gum allows us, after suitable image processing for contrast improvement, to measure with a very good precision the displacement field inside the rubber during the indentation using Digital Volume Correlation. The measured displacement is compared with the result of an axisymmetric finite element modeling calculation reproducing the experimental configuration. The close correlation between measured displacements and finite element modeling shows that the proposed method is suitable for studying rubber/road contact.

{"title":"Experimental method for rubber deformation analysis using in situ X-ray tomography and digital volume correlation with FEM validation","authors":"J. Lachambre , A. Sibellas , J. Adrien , J. Papillon , R. Bruant , G. Maurel , E. Maire","doi":"10.1016/j.tmater.2024.100045","DOIUrl":"10.1016/j.tmater.2024.100045","url":null,"abstract":"<div><div>The grip and rolling resistance of car wheel tires on a road surface is strongly influenced by the mechanical properties of the rubber and especially by the deformation of this rubber during indentation by the road asperities. This paper presents the results of a simplified indentation experiment. A cylindrical block of rubber containing microstructural markers is indented by a sphere in situ inside an X-ray Computed Tomograph. The presence of markers intentionally added to the rubber gum allows us, after suitable image processing for contrast improvement, to measure with a very good precision the displacement field inside the rubber during the indentation using Digital Volume Correlation. The measured displacement is compared with the result of an axisymmetric finite element modeling calculation reproducing the experimental configuration. The close correlation between measured displacements and finite element modeling shows that the proposed method is suitable for studying rubber/road contact.</div></div>","PeriodicalId":101254,"journal":{"name":"Tomography of Materials and Structures","volume":"7 ","pages":"Article 100045"},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

New watershed methods for isolating and characterizing discrete objects in 3D data sets

Tomography of Materials and Structures

Pub Date : 2024-11-28 DOI: 10.1016/j.tmater.2024.100043

Richard A. Ketcham

This paper introduces new algorithms for conducting and improving watershed analysis, implemented with the particular goal of improving the ability to measure the shapes of mineral grains to be subsequently be analyzed by mass spectrometry. This application requires a high degree of accuracy and fidelity in terms of both separating all touching grains and preserving their shapes. The algorithms are designed to take advantage of a vector-based programming environment. A new implementation of the Euclidean distance transform utilizes the fact that the distance from any adjacent pair of voxels to the nearest boundary must be within one voxel of each other. In practice, however, this algorithm is outperformed by a smoothed approximate distance transform that is faster to compute and results in less irregular watershed boundaries. A one-pass rainfall-based watershed algorithm is introduced that runs in linear time with the number of segmented voxels, and requires no priority queue. Unlike marker-based watershed algorithms based on the basin-filling approach, the rainfall approach finds watersheds associated with all local maxima in the distance map, even if a marking algorithm is used. A post-watershed smoothing algorithm improves watershed boundaries and eliminates small spurious watersheds. The one-pass watershed and post-watershed smoothing algorithms run in times superior or comparable to basin-fill watershed algorithms implemented in other environments, and offers excellent ability to separate touching objects efficiently while placing watershed boundaries that maximize the preservation of details of particle shape. Further time improvement could come from implementing them in a vector-based environment that allows explicit multi-threading.

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