INDUSTRY REPORT 03 · INDUSTRIAL CT
closed impeller · drag to rotate · illustrative AI reconstruction, not a scan
A closed impeller carries its most consequential surfaces where no probe stylus, no camera, and no fringe projector will ever reach: the flow passages sealed between hub and shroud. For most of manufacturing history the honest options were to cut the part apart or to trust the process. Industrial CT is the third option — the one inspection technology whose reach does not depend on line of sight — and it has quietly become the reference method for castings and additive parts whose critical geometry is internal. This report covers what computed tomography actually produces, what it is trusted for today, where its numbers go soft, and why the labs that own a scanner drown in evaluation time rather than scan time.
Contents
Every other instrument in the metrology lab is a line-of-sight instrument. A CMM stylus must physically reach the feature; a structured-light sensor must see it from two camera angles at once; even a borescope only inspects the corridor it crawls through. The impeller at the top of this page defeats all of them by design: the passages between the vanes are bounded above by the shroud, and there is no direction from which a probe or a projected fringe enters. The same is true of cored oil galleries in a cylinder head, conformal cooling channels in an additively built mold insert, and the sealed interior of any assembled product. CT scanning does not care, because X-rays pass through the part, and the measurement is built from what the part did to them on the way.
It helps to be precise about what the machine actually produces, because it is not a point cloud. During a scan the part rotates on a stage while the detector records hundreds or thousands of two-dimensional projections — greyscale radiographs, each one a map of how much the beam was attenuated along every ray through the part. A reconstruction algorithm (for the standard cone-beam geometry, the Feldkamp method and its descendants) folds those projections into a three-dimensional grid of grey values. Each cell of that grid is a voxel — a volumetric pixel — and its grey value encodes the local X-ray attenuation, which tracks density and material. That volume is the raw product of computed tomography. Note what it is not: it is not yet a surface, not yet a dimension, not yet a defect count. It is a block of numbers in which material is bright and air is dark and the boundary between them is a gradient several voxels wide.
Surface determination is the step that turns grey values into a measurable boundary, and it deserves more respect than it usually gets. The classic starting point is the ISO-50 rule: take the grey value halfway between the material peak and the background peak of the histogram, and call that iso-surface the part. Modern software refines this locally — adapting the threshold region by region to the actual gradient — and interpolation across the gradient locates the boundary to a small fraction of a voxel, which is why CT can measure features finer than its voxel size suggests. But here is the part that matters for anyone signing an inspection report: every downstream number inherits the surface-determination decision. Shift the threshold and every hole in the part grows while every pin shrinks, wall thicknesses change in lockstep, and marginal pores swell or vanish. Two analysts with the same volume and different surface settings will produce two different parts. That makes surface determination a process parameter to be chosen, validated, and written down — not a default to be accepted. Once the surface exists, the familiar coordinate-metrology machinery takes over — alignment, feature fitting, GD&T — and the same least-squares-versus-Chebyshev choices that govern any fitted feature apply to CT-extracted geometry too.
One more piece of vocabulary discipline. Voxel size is set by geometry: the closer the part sits to the X-ray source relative to the detector, the higher the magnification and the finer the voxels — so small parts scan fine and large parts scan coarse, and a claim like “5 µm accuracy” with no part size attached is marketing, not metrology. And voxel size is not resolution: focal-spot blur, detector characteristics, and artifacts all limit what the volume genuinely resolves, which is why the German guideline family for dimensional CT (more on it in section 3) treats structural resolution as its own characteristic, separate from voxel count. A defect that spans one or two voxels is a rumor, not a measurement.
Porosity and inclusion analysis is the flagship, especially in aluminum die casting. Gas porosity, shrinkage porosity, and foreign-material inclusions are internal defects by definition — the whole failure mode lives below the surface — and CT is the only method that maps them in 3D without destroying the part. Mature porosity analysis does not ask “is there porosity?” (the answer in a die casting is essentially always yes); it asks whether the pores exceed an agreed acceptance class: limits on pore size, local concentration, and — critically — distance to a machined surface, because a pore that the finishing cut will open into a sealing face is a leak path, while the same pore buried mid-wall may be irrelevant. In German-speaking foundry practice the anchors for this are the foundry association's reference-sheet family — P 201 and P 202 on volume deficits, with P 203 extending the evaluation methodology to CT specifically. I am confident of the family's role in drawing callouts; check the current editions before writing one into a purchase spec. The classifiers also use pore shape: compact, roundish voids read as gas porosity, sprawling dendritic ones as shrinkage — different root causes, different corrective actions at the machine.
turbocharger housing (AI reconstruction) — cored internal passages make wall thickness a blind measurement for every line-of-sight method
Wall thickness on complex castings is the second pillar, and the turbo housing above shows why. A cored casting's wall thickness is the distance between an outer surface you can see and an inner surface you cannot; if the core shifted during pouring, the wall is thin on one side and thick on the other, and nothing visible from outside betrays it. CT wall-thickness analysis computes the local thickness over the entire part and paints it as a colormap — full-field, including every cored passage — which turns core shift from an occasional sectioning discovery into a routinely monitored characteristic. For thermally and pressure-loaded parts like turbine and turbocharger housings, minimum wall is often the life-limiting dimension, and CT is the only instrument that measures it everywhere.
Assembly inspection exploits the same physics in the other direction: scan the finished, assembled product and inspect it in its working state — seated seals, engaged clips, solder joints, wire routings — without taking it apart, which for many failure modes is the only meaningful time to look. A documented example from my research corpus: ZKW, the lighting-systems supplier, brought CT in-house with a METROTOM and ZEISS INSPECT X-Ray to inspect assembled headlights in batches of more than twenty, after external CT services had meant waits of three to four weeks (vendor-sourced case study). Multi-material assemblies — plastics, metals, electronics in one scan — are exactly the case where sectioning destroys the evidence it is looking for.
Additive manufacturing inspection may be the application that made dimensional CT mainstream, because AM removed the alternative. The entire argument for printing a part is often its internal geometry — conformal cooling channels, internal lattices, consolidated flow passages — and none of it is inspectable by any line-of-sight method after the build. CT verifies channel cross-sections, finds trapped powder, and measures lattice struts; independent researchers at Brno UT/CEITEC, for instance, scripted the creation of inspection elements across the many struts of laser-melted lattice structures measured by micro-CT — a task nobody could do by hand at scale (independent paper, via research corpus). On the standards side, ASTM E3166 catalogs nondestructive examination methods for metal AM aerospace parts, CT prominent among them. The honest caveat: the metals AM favors vary in X-ray friendliness — aluminum and titanium scan well at moderate energies, dense nickel superalloys demand more voltage and yield noisier volumes.
A pattern worth naming across all four: the operations that trust CT most got there by correlation, not faith. The die caster TCG UNITECH, which replaced several separate inspections with one CT scan per part, reportedly spent months correlating CT porosity results against destructive microscopy and other methods before relying on them (vendor-sourced case study). That is not a weakness of the method. That is what adopting any new reference method correctly looks like.
CT dimensional measurement is real — parts are accepted and rejected on it every day — but anyone selling it as a solved problem is skipping the interesting part. The acceptance-testing framework for CT is younger and messier than the tactile world's, and the physics contributes biases that a CMM user never has to think about.
The standards situation. Tactile CMMs have had ISO 10360 acceptance and reverification tests for decades. For CT, the working framework since roughly 2010 has been the German guideline family VDI/VDE 2630 — Part 1.2 on the influence quantities, Part 1.3 on applying ISO 10360-style tests to CT sensors, Part 2.1 on determining task-specific measurement uncertainty. An actual ISO acceptance standard, ISO 10360-11 for coordinate measuring systems using X-ray CT, arrived only in 2023. If that trajectory sounds familiar, it is the same story I told about structured light in report 01 — a German guideline carrying the field for a decade until an ISO part lands — except CT's ISO part is two years younger still, and the paperwork in circulation will reference the VDI framework for years yet. Meanwhile the NDT community has its own, older CT standards — ASTM E1441 (guide) and E1570 (practice) for CT examination, ASTM E1695 for system performance, and the ISO 15708 series on radiographic CT methods — which govern defect detection, not dimensional acceptance. Two communities, two literatures, one machine: a lab that quotes an NDT standard as evidence of dimensional capability, or vice versa, is waving the wrong certificate.
The artifacts. CT's systematic errors have names, and they bias numbers rather than merely blurring pictures. The table gives the plain-language versions; all of this is standard CT physics, stated as general knowledge.
| Artifact | What causes it | What it does to your numbers | First-line countermeasures |
|---|---|---|---|
| Beam hardening | The tube emits a spectrum; soft photons absorb first, so the beam “hardens” as it penetrates — but reconstruction assumes it didn't | Edges read denser than cores (“cupping”); apparent surfaces shift; streaks between dense features | Physical pre-filtration (copper/tin), correction algorithms, higher tube voltage |
| Scatter | Photons deflected inside the part or cabinet land on the wrong detector pixels | A haze that lifts grey values unevenly; contrast loss; surface bias that changes with part orientation | Collimation, geometry, software scatter correction |
| Cone-beam artifact | The standard fast reconstruction is only exact in the detector's central plane | Flat faces near the top and bottom of the volume blur or dish | Put critical features near mid-height; helical trajectories where available |
| Ring artifacts | Miscalibrated or defective detector pixels rotate with the reconstruction | Concentric rings that mimic density variation | Detector recalibration; detector-shift scanning modes |
| Penetration starvation | Some ray directions cross too much metal to deliver signal | Noisy, dished surfaces along the long axis; unusable regions | Reorient the part, raise voltage and filtration, accept longer scans |
The practical consequence of the first two rows is worth underlining: beam hardening and scatter do not add random noise you can average away — they move surfaces, systematically, by amounts that depend on material, geometry, and orientation. This is why a CT machine's sphere-distance acceptance test — typically run on a friendly calibrated artifact — bounds very little about your actual casting, and why task-specific uncertainty is the concept that matters. The honest route to a defensible number is the substitution approach codified in VDI/VDE 2630 Part 2.1 (the CT sibling of the ISO 15530-3 method): measure a calibrated twin of your part, on your machine, with your exact scan recipe and evaluation template, and let the observed errors set the uncertainty. It also reframes measurement-systems analysis: in a CT gage study, the “operator” barely matters compared to the parameter set — scan recipe, surface determination, evaluation template. A classical crossed R&R that varies people while holding parameters fixed will flatter a CT process; vary the things that actually vary.
The economics of industrial CT changed shape over the last decade, and the shape it took surprises buyers. Scanning got fast — small-part scans that once took an hour run in minutes, and reconstruction that once tied up a workstation overnight finishes on a GPU before the next part is fixtured. Capital is still serious money: industrial systems run from roughly $100k to well past $1M depending on energy and cabinet size (vendor-sourced cost classes, consistent with report 01), and service bureaus fill the gap below that at a few hundred dollars a scan. But the line item that actually swells as a CT program succeeds is none of these. It is analyst evaluation time.
Consider what happens after every scan of a recurring part: someone opens a multi-gigabyte volume, runs surface determination, aligns to the datum structure, applies the porosity rule set, checks wall-thickness zones, evaluates the dimensional characteristics, and assembles a report. Done by hand, that is tens of minutes to hours of skilled attention per part — repeated identically for part after part. The pattern in my own buyer research is blunt: the CT teams feeling the most pain are precisely the ones with recurring multi-part or defect workflows — large data, repeated segmentation and evaluation, high analyst hours, and complex exports into quality systems. The scanner keeps up; the human doesn't. ZKW's batches of twenty-plus assemblies per run only work because the evaluation is templated, and Festo — facing CT reproducibility across a portfolio it counts at 33,000 products and thousands of part variants — distributes a common evaluation plan globally and uses an automated defect-detection application to classify porosity (vendor-sourced case studies).
This is where evaluation automation earns its keep, and it comes in escalating grades: parametric project templates that re-execute an entire evaluation on the next volume; batch processing that runs the template across a directory of scans overnight; automated defect rules scoped by region of interest so the software flags only what the acceptance class actually cares about; and scripted pipelines that push results straight into the quality system. The tooling has been moving the same direction — the 2026 release of ZEISS INSPECT X-Ray, to take the platform I work in, expanded its Python scripting API and Q-DAS statistical export alongside multi-material and defect-analysis improvements (release notes, via research corpus). One honest caution belongs in the same paragraph: automation does not make a measurement more true. It makes the same decisions consistently, at scale, and moves the analyst's hours from repetition to exceptions — which is exactly what you want, provided the templated decisions were validated first. Automating an unvalidated surface-determination setting just manufactures wrong answers faster. The scripted-evaluation work I show on the examples page is this category of work: the pipeline is the deliverable, and the validation evidence rides with it.
Readers of report 01 will remember the division of labor that emerged from the foundry evidence: tactile CMMs hold the datums and tight primitives, full-field optical owns the freeform outside, and CT owns the inside. Nothing in this report revises that doctrine — it fills in CT's side of it. The two full-field technologies are complements, not competitors, and the boundary between them is nearly clean: structured light is faster and cheaper per part on everything it can see, and it can see nothing it doesn't have line of sight to; CT sees everything and pays for it in cycle time, capital, and artifact management.
The genuinely interesting question is the overlap case: CT reconstructs the outside of the part too, so a part that needs CT anyway — for porosity, say — can in principle carry its external dimensional evaluation in the same volume and skip a second measurement. Sometimes that is the right call, and single-scan combined workflows are a real selling point of modern CT software. But it should be a decision, not a drift: external surfaces measured through CT carry the surface-determination and artifact budget of section 3, and on dense or awkwardly proportioned parts an optical scan of the exterior will beat the CT-derived exterior on both uncertainty and cost. Physics draws the outer boundary of the whole method: aluminum, magnesium, plastics, and small steel parts are CT-friendly; large dense steel sections starve the beam, which is why the big steel castings of report 01 get optical outsides and sampled, high-energy — or destructive — insides. Match the modality to the material and the tolerance, one characteristic at a time.
Every recurring theme in this report converges on the same discipline: the decisions that determine what a CT number means are all upstream of the number, and they should exist on paper before the first acceptance decision. The list is short.
Everything on that list is pipeline work — the evaluate-decide-deliver-defend chain that starts where the scanner stops. If your lab has the volumes and the colormaps but the decisions above were made by software defaults, that is exactly the work I offer — starting with a fixed-fee pipeline assessment: hello [at] metrologymaven [dot] io.
Dimensional-CT standards: ISO 10360-11:2023 (acceptance/reverification tests, CT-based coordinate measuring systems) · VDI/VDE 2630 guideline family — Blatt 1.2 (influence quantities), Blatt 1.3 (ISO 10360-style testing for CT sensors), Blatt 2.1 (task-specific measurement uncertainty); VDI, Düsseldorf · ISO 15530-3 (uncertainty by calibrated workpieces — the substitution method Blatt 2.1 parallels)
NDT-side standards: ASTM E1441 (Standard Guide for Computed Tomography) · ASTM E1570 (Standard Practice for CT Examination) · ASTM E1695 (CT system performance measurement) · ISO 15708 series (radiation methods for computed tomography) · ASTM E3166 (NDE of metal additively manufactured aerospace parts)
Porosity acceptance: BDG (German foundry association) reference sheets P 201/P 202 (volume deficits of castings) and P 203 (porosity analysis by CT) — cited for their role; consult current editions before contractual use
Vendor case studies & corpus: ZEISS success stories: ZKW (assembled-headlight CT, external lead times), TCG UNITECH (CT correlation before trust) · ZEISS / Festo (automated defect classification at portfolio scale) · ZEISS: CT cost classes · ZEISS INSPECT X-Ray Release 2026 notes (Python API, Q-DAS, defect-analysis expansion) · Brno UT/CEITEC micro-CT lattice automation study (independent paper) · practice research corpus on ZEISS INSPECT workflows and buyers, 2026 (unpublished; basis for the “evaluation is the bottleneck” pattern)