Measurement accuracy in X-ray computed tomography metrology: Toward a systematic analysis of interference effects in tomographic imaging

Hiller, Jochen; Hornberger, Peter

doi:10.1016/j.precisioneng.2015.12.003

Cited by 56 publications

(28 citation statements)

References 27 publications

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“…In all these previous works reference calibration objects with regular geometries were used. The same types of reference objects with regular geometries are found in the literature published for other authors [23][24][25][26][27]. In the present work the authors propose a real object with complex 3D geometry, which is an innovation with respect to the previous works found in the literature.…”

Section: Introductionmentioning

confidence: 57%

“…based on local threshold). On the one hand, by applying the CT1 method, reference values for most of the elements to be measured are usually needed in order to adjust the ISO factor [27], which could be particularly difficult when measuring 3D complex geometries, as in the presented work. On the other hand, the edge detection technique based on the Canny algorithm (CT2) provides a good edge location capability, less dependent on the geometry of the measured part.…”

Section: Systematic Error Analysismentioning

confidence: 99%

“…Different measuring strategies are also compared in [25], where three different inspection software packages for volume and surface data analysis were applied. Additionally, the authors in [26] evaluated and quantified the repeatability of post-processing settings, such as data fitting, the definition of the datum system and surface determination, which is also analyzed in [27,28]. As some of these works show, the surface extraction technique used is one of the most influent factors in the final measurement uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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

Torralba

Jiménez

Yagüe‐Fabra

et al. 2018

Int J Adv Manuf Technol

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The number of industrial applications of Computed Tomography (CT) for dimensional metrology in 10 0-10 3 mm range has been continuously increasing, especially in the last years. Due to its specific characteristics Computed Tomography has the potential to be employed as a viable solution for measuring 3D complex micro-geometries as well (i.e. in the sub-mm dimensional range). However, there are different factors that may influence the CT process performance, being one of them the surface extraction technique used. In this paper two different extraction techniques are applied to measure a complex miniaturized dental file by CT in order to analyze its contribution to the final measurement uncertainty in complex geometries at the mm to sub-mm scales. The first method is based on a similarity analysis: the threshold determination; while the second one is based on a gradient or discontinuity analysis: the 3D Canny algorithm. This algorithm has proven to provide accurate results in parts with simple geometries, but its suitability for 3D complex geometries has not been proven so far. To verify the measurement results and compare both techniques, reference measurements are performed on an optical coordinate measuring machine (OCMM). The systematic errors and uncertainty results obtained show that the 3D Canny adapted method slightly lower systematic deviations and a more robust edge definition than the local threshold method for 3D complex micro-geometry dimensional measurements.

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Section: Introductionmentioning

confidence: 57%

Section: Systematic Error Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Torralba

Jiménez

Yagüe‐Fabra

et al. 2018

Int J Adv Manuf Technol

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“…Typically, characterizing the geometric accuracy of a part relies on one of the following approaches: evaluation based on visual assessment [5], measurement based on specific landmarks on the part [7], and GD&T measurements using coordinate measuring machines [8]. These approaches provide only limited information of the overall geometric accuracy of the part and may not be capable of capturing the subtle difference parts of complex shapes fabricated using AM [1].…”

Section: Existing State-of-the-art and Research Gapsmentioning

confidence: 99%

“…Measuring part geometric accuracy is typically performed with various approaches, including ultrasonic, X-ray tomography, and laser scanning [8][9][10][11][12]. The resulting data are typically analyzed using statistical models (e.g., response surface regression and analysis of variance [13][14][15][16]) to develop an empirical mapping of the relationship between input process parameters and geometric accuracy.…”

Section: Data Science Approaches (Empirical Modeling Of Geometric Accmentioning

confidence: 99%

Quantifying Geometric Accuracy With Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts

Khanzadeh

Rao

Jafari-Marandi

et al. 2017

Journal of Manufacturing Science and Engineering

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Although complex geometries are attainable with additive manufacturing (AM), a major barrier preventing its use in mission-critical applications is the lack of geometric accuracy of AM parts. Existing geometric dimensioning and tolerancing (GD&T) characteristics are defined based on simple landmark features, and thus, need to be customized to capture the subtle difference in parts with complex geometries. Hence, the objective of this work is to quantify the geometric deviations of additively manufactured parts from a large data set of laser-scanned coordinates using an unsupervised machine learning (ML) approach called the self-organizing map (SOM). The central hypothesis is that clusters recognized by the SOM correspond to specific types of geometric deviations, which in turn are linked to certain AM process conditions. This hypothesis is tested on parts made while varying process conditions in the fused filament fabrication (FFF) AM process. The outcomes of this research are as follows: (1) visualizing and quantifying the link between process conditions and geometric accuracy in FFF and (2) significantly reducing the amount of point cloud data required for characterizing of geometric accuracy. The significance of this research is that this unsupervised ML approach resulted in less than 3% of over 1 million data points being required to fully quantify the part geometric accuracy.

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In silico approach towards neuro‐occlusal rehabilitation for the early correction of asymmetrical development in a unilateral crossbite patient

Ortún‐Terrazas

Cegoñino

Palomar

2023

Numer Methods Biomed Eng

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Measurement accuracy in X-ray computed tomography metrology: Toward a systematic analysis of interference effects in tomographic imaging

Cited by 56 publications

References 27 publications

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

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

Quantifying Geometric Accuracy With Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts

In silico approach towards neuro‐occlusal rehabilitation for the early correction of asymmetrical development in a unilateral crossbite patient

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