Comparison of surface extraction techniques performance in computed tomography for 3D complex micro-geometry dimensional measurements

Torralba, Marta; Jiménez, R.; Yagüe‐Fabra, José A.; Ontiveros, S.; Tosello, Guido

doi:10.1007/s00170-018-1950-9

Cited by 12 publications

(7 citation statements)

References 31 publications

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“…The number of industrial applications of computed tomography (CT) for dimensional metrology has increased significantly, and CT is employed for measuring 3D complex microgeometries [48]. Buratti et al [49] indicated that CT played a key role in 3D coordinate metrology as an alternative to conventional coordinate measuring machines and enhanced and validated an analytical method for optimising imaging parameters.…”

Section: Non-traditional Measurement Equipment and Methodsmentioning

confidence: 99%

An Iterative Minimum Zone Algorithm for assessing cylindricity deviation

Liu

Cao

et al. 2023

Measurement

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Section: Non-traditional Measurement Equipment and Methodsmentioning

confidence: 99%

An Iterative Minimum Zone Algorithm for assessing cylindricity deviation

Liu

Cao

et al. 2023

Measurement

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“…Expanded measurement uncertainties with level of confidence of 95% ( U 95 ) were calculated according to ISO 14253-2 [ 16 , 20 ] for each one of the measurements carried out over the air wedge gap of the Al/Ti multi-material standard. This standard takes into account the uncertainty contributions related to the CT system length measurement errors with respect to the reference measurements performed with the micro-CMS Zeiss F35, the repeatability of the measurement procedure assessed from five repetitions of the full measurement process (scan, reconstruction, surface determination and measurement) and other uncertainty contributors related to the samples material and temperature stability during the CT measuring process.…”

Section: Methodsmentioning

confidence: 99%

“…One based on the Canny algorithm [ 13 ] and another based on the Deriche algorithm [ 14 , 15 ]. These algorithms have been tested using parts with different geometries: round, flat and freeform [ 16 ]. They have also been tested using a polymer multi-material standard [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Gradient-Based Algorithm for Surface Determination in Multi-Material Gap Measurements by X ray Computed Tomography

et al. 2020

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X-ray computed tomography is one of the most promising measurement techniques for the dimensional evaluation of industrial components. However, the inherent complexity of this technology also involves important challenges. One of them is to develop surface determination algorithms capable of providing measurement results with better accuracy in any situation—for example, for single and multi-material parts, inner and outer geometries, with and without image artefacts, etc.—and reducing user influence. The surface determination is particularly complex in the case of multi-material parts, especially when they are separated by small air gaps. In previous works, two gradient-based algorithms were presented, that showed less measurement variability throughout the whole part, and reduced the computational cost and operator influence compared to threshold-based algorithms. This work focuses on the evaluation of the performance of these algorithms when used in a scenario so complex that parts of it are made of one or more materials (metal–metal and polymer–metal) with gaps inside. For this purpose, a set of multi-material reference standards is used. The presented gradient-based algorithms show measurement errors comparable to commercial threshold-based algorithms, but with the capability of obtaining accurate measurements in smaller gaps, apart from reducing the user influence on the measurement process.

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“…Accurate segmentation has long been a challenge across various scientific domains, from medical CT imaging to material sciences and engineering (Withers et al, 2021). Global segmentation methods, such as manual histogram or watershed thresholding, have long been go-to solutions for the segmentation of X-ray CT tomographic images.…”

Section: Comparison With Other Segmentation Approachesmentioning

confidence: 99%

Using internal standards in time-resolved X-ray micro-computed tomography to quantify grain-scale developments in solid-state mineral reactions

Rizzo,

Freitas,

Gilgannon

et al. 2024

Solid Earth

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Abstract. X-ray computed tomography has established itself as a crucial tool in the analysis of rock materials, providing the ability to visualise intricate 3D microstructures and capture quantitative information about internal phenomena such as structural damage, mineral reactions, and fluid–rock interactions. The efficacy of this tool, however, depends significantly on the precision of image segmentation, a process that has seen varied results across different methodologies, ranging from simple histogram thresholding to more complex machine learning and deep-learning strategies. The irregularity in these segmentation outcomes raises concerns about the reproducibility of the results, a challenge that we aim to address in this work. In our study, we employ the mass balance of a metamorphic reaction as an internal standard to verify segmentation accuracy and shed light on the advantages of deep-learning approaches, particularly their capacity to efficiently process expansive datasets. Our methodology utilises deep learning to achieve accurate segmentation of time-resolved volumetric images of the gypsum dehydration reaction, a process that traditional segmentation techniques have struggled with due to poor contrast between reactants and products. We utilise a 2D U-net architecture for segmentation and introduce machine-learning-obtained labelled data (specifically, from random forest classification) as an innovative solution to the limitations of training data obtained from imaging. The deep-learning algorithm we developed has demonstrated remarkable resilience, consistently segmenting volume phases across all experiments. Furthermore, our trained neural network exhibits impressively short run times on a standard workstation equipped with a graphic processing unit (GPU). To evaluate the precision of our workflow, we compared the theoretical and measured molar evolution of gypsum to bassanite during dehydration. The errors between the predicted and segmented volumes in all time series experiments fell within the 2 % confidence intervals of the theoretical curves, affirming the accuracy of our methodology. We also compared the results obtained by the proposed method with standard segmentation methods and found a significant improvement in precision and accuracy of segmented volumes. This makes the segmented computed tomography images suited for extracting quantitative data, such as variations in mineral growth rate and pore size during the reaction. In this work, we introduce a distinctive approach by using an internal standard to validate the accuracy of a segmentation model, demonstrating its potential as a robust and reliable method for image segmentation in this field. This ability to measure the volumetric evolution during a reaction with precision paves the way for advanced modelling and verification of the physical properties of rock materials, particularly those involved in tectono-metamorphic processes. Our work underscores the promise of deep-learning approaches in elevating the quality and reproducibility of research in the geosciences.

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Comparison of surface extraction techniques performance in computed tomography for 3D complex micro-geometry dimensional measurements

Cited by 12 publications

References 31 publications

An Iterative Minimum Zone Algorithm for assessing cylindricity deviation

An Iterative Minimum Zone Algorithm for assessing cylindricity deviation

Assessment of Gradient-Based Algorithm for Surface Determination in Multi-Material Gap Measurements by X ray Computed Tomography

Using internal standards in time-resolved X-ray micro-computed tomography to quantify grain-scale developments in solid-state mineral reactions

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