On the uncertainty of surface determination in x-ray computed tomography for dimensional metrology

Abstract

With x-ray computed tomography (CT) it is possible to evaluate the dimensions of an object’s internal and external features non-destructively. Dimensional measurements evaluated via x-ray CT require the object’s surfaces first be estimated; this work is concerned with evaluating the uncertainty of this surface estimate and how it impacts the uncertainty of fitted geometric features. The measurement uncertainty due to surface determination is evaluated through the use of a discrete ramp edge model and a Monte Carlo simulation. Based on the results of the Monte Carlo simulation the uncertainty structure of a coordinate set is estimated, allowing individual coordinate uncertainties to be propagated through the geometry fit to the final measurement result. The developed methodology enables the uncertainty due to surface determination to be evaluated for a given measurement task; the method is demonstrated for both measured and simulated data.

X-ray Computed Tomography InstrumentPerformance Evaluation, Part I: Sensitivity to Detector Geometry Errors

“…We note that the final surface point cloud obtained is strongly dependent on the choice of gray-value threshold. The often-used ISO-50 threshold [14] does not necessarily capture the entire surface in the presence of large geometry errors; we therefore manually selected a threshold to enclose as much of the reconstructed sphere as possible.…”

Characterization of surface topography of 3D printed parts by multi-scale analysis

“…The information is stored as a numerical value (gray level) in a volume element called a voxel [26]. Surface extraction from these 3D data (also referred to as 3D edge detection) is the first necessary stage for metrology applications [7,14], and is strongly linked to the voxel size which represents the tomography resolution [13]. As a result, CT measurement gives an estimate of the part geometry depending on the tomography resolution [13].…”

“…As a result, CT measurement gives an estimate of the part geometry depending on the tomography resolution [13]. Some studies show that uncertainty in surface extraction can be reduced by making use of a sub-voxel resolution method which numerically improves CT resolution [26,14]. Uncertainty can achieve 1/10 of the voxel size when sub-voxel resolution is used [5], which makes CT applicable for dimensional metrology.…”

Multi-scale surface characterization in additive manufacturing using CT

“…The information is stored as a numerical value (gray level) in a volume element called voxel [10]. Surface extraction from these 3D data (also referred to as 3D edge detection) is the first necessary stage for metrology applications [11,12], and is strongly linked to the voxel size which represents the tomography resolution [13]. Some studies show that uncertainty in surface extraction can be reduced by making use of a sub-voxel resolution method which numerically improves CT resolution [12,10].…”

“…Surface extraction from these 3D data (also referred to as 3D edge detection) is the first necessary stage for metrology applications [11,12], and is strongly linked to the voxel size which represents the tomography resolution [13]. Some studies show that uncertainty in surface extraction can be reduced by making use of a sub-voxel resolution method which numerically improves CT resolution [12,10]. Uncertainty can achieve 1/10 of the voxel size when sub-voxel resolution is used [14], which makes CT applied for dimensional metrology applications.…”