Overview​

The workflow consists of four stages:

1. Threshold segmentation: Extract the material region from the volumetric grayscale data as a binary mask.
2. Surface generation and cleanup: Convert the mask to a triangle mesh surface, apply remeshing and geometric repairs.
3. Wall thickness analysis: Run the ray-casting analysis on the surface to compute per-vertex thickness values.
4. Results export and visualisation: Write statistics to disk and configure colour-mapped display.

This pipeline is well-suited to batch processing scenarios — for instance, comparing wall thickness distributions across multiple production parts scanned in the same session.

Threshold Segmentation​

The first step isolates the material from the background by applying an intensity threshold to the grayscale volume. The segmentation quality at this stage directly affects the accuracy of the subsequent surface and therefore the wall thickness values. Thresholds that include background noise will inflate surface area and generate erroneous thin-wall regions at the part boundary.

threshold_params = api.ThresholdParams()
threshold_params.lower_threshold = 31724.617
threshold_params.upper_threshold = 65156
threshold_params.fill_cavities = True
threshold_params.filter_regions = True
threshold_params.keep_largest = True

Key parameter choices:

• fill_cavities: Fills enclosed interior voids in the binary mask, ensuring the segmented object represents the full material region rather than a hollow shell. Disable this only if interior void geometry is relevant to the analysis.
• keep_largest: Retains only the largest connected component, discarding small background fragments or debris that fall within the threshold range.
• filter_regions: Removes disconnected region noise below a minimum size.

Surface Generation and Cleanup​

Once the mask is created, it is converted to a triangle mesh surface for analysis:

mask_to_surface_params = api.MaskToSurfaceParams()
mask_to_surface_params.resolution_reduction = True
mask_to_surface_params.xy_resolution_reduction = 2
mask_to_surface_params.smoothing = True
mask_to_surface_params.smooth_iterations = 50
mask_to_surface_params.triangle_reduction = True
mask_to_surface_params.triangle_reduction_percent = 90

The triangle_reduction_percent value of 90 reduces the triangle count by 90%, which is appropriate for smooth, industrial parts where the mesh detail is well above the scale of the wall thickness variation of interest. For more geometrically complex parts with fine features, reduce this value to preserve surface fidelity.

After mesh extraction, two additional cleanup steps are applied:

Remeshing (surface_operations.remesh) regularises element size and improves mesh uniformity. This reduces artefacts in the wall thickness analysis caused by highly irregular triangulations, where very elongated triangles can produce sampling bias.

Surface diagnostics and repair (surface_operations.fix_surface) corrects common mesh pathologies: non-manifold edges, self-intersections, degenerate triangles, and open boundaries. The wall thickness ray-casting algorithm assumes a closed, manifold surface. Unrepaired mesh defects in these regions produce undefined or outlier thickness values.

Wall Thickness Analysis​

With a clean, closed surface, the wall thickness analysis can proceed. The ray-casting method is used here:

wall_thickness_params = api.WallThicknessParams()
wall_thickness_params.method = api.WallThicknessMethod.RayCasting
wall_thickness_params.max_wall_thickness = 20.0
wall_thickness_params.search_angle = 20.0

Ray Casting vs. Shrinking Sphere​

The ray casting method fires a ray from each surface vertex along its outward normal (and inward normal) and records the distance to the first opposing surface intersection. This is fast and accurate for parts where inner and outer surfaces are roughly parallel — sheet metal, extrusions, simple castings, and similar geometry.

The shrinking sphere method instead finds the largest inscribed sphere centred on the normal axis that contacts both surfaces. This yields more physically meaningful results for curved walls and organic geometries where ray casting may underestimate thickness on the concave side of a curved section. The trade-off is substantially higher computation time.

For most industrial CT workflows, ray casting is the appropriate first choice. Switch to shrinking sphere when ray casting produces visible artefacts (typically seen as anomalously thin regions at corners or curved transitions).

Setting the Maximum Wall Thickness​

The max_wall_thickness limit constrains the search distance. Setting this to a value slightly above the known nominal wall thickness improves performance without excluding any physically valid measurements. For parts with unknown thickness range, set it to 0 (unlimited) for the first run, observe the maximum value from the resulting statistics, and then re-run with a constrained value for batch processing.

Results and Visualisation​

On completion, the analysis returns a full statistical summary:

results = analysis_operations.get_wall_thickness_analysis_results(wt_name)
print(f"Mean wall thickness: {results.statistics.mean_value:.3f} mm")

The colour-mapped surface display uses a lookup table to map thickness values to colour. The configuration below uses a reverse rainbow scale with custom colours for out-of-range regions, which is effective for immediately identifying both critically thin regions (below the min range) and excess material (above the max range):

analysis_display_settings.lookup_table_type = api.LookupTableType.ReverseRainbow
analysis_display_settings.range = [1.0, 20.0]
analysis_display_settings.below_min_range_color = [0.6, 0.0, 1.0]  # Violet for thin regions
analysis_display_settings.above_max_range_color = [1.0, 0.6, 0.6]  # Light red for thick regions

Statistical results are written to disk for reporting or further analysis:

analysis_operations.write_wall_thickness_results_to_disk(
    results,
    r'C:\Samples\wall_thickness.txt'
)

Video Tutorial​

The complete workflow is demonstrated in the video below:

Related Resources​

• Wall Thickness Analysis — GUI Reference
• Wall-Thickness Analysis Workflow — Scripting Tutorial
• Python Scripting API Overview
• Scripting Quick Reference

Overview​

The complete workflow covered here proceeds as follows:

1. Reconstruct a 3D volume from a sequence of cone beam projection images using the FDK (Feldkamp–Davis–Kress) algorithm.
2. Apply post-reconstruction filtering and render properties to improve visual interpretability.
3. Run void/inclusion analysis to detect and characterise internal defects.
4. Export per-defect statistics and aggregate porosity metrics to disk.

The same workflow applies to any industrial CT dataset where projection geometry is known — castings, welds, additive manufactured parts, composites, and similar materials.

Cone Beam CT Reconstruction​

The FDK algorithm requires accurate knowledge of the scanning geometry. Errors in source-to-detector distance, source-to-isocenter distance, or detector pixel size propagate directly into the reconstructed voxel dimensions and therefore into all downstream measurements. Before running reconstruction, verify these parameters against your scanner's calibration data.

The key geometry parameters are:

If your detector is not perfectly aligned with the rotation axis, the offset parameters (projection_offset_x/y, source_offset_x/y, out_of_plane_angle, in_plane_angle) allow you to compensate for translational and rotational misalignment before reconstruction rather than in post-processing.

Reconstruction Filter​

The Hann filter (hann_cut_frequency) controls the high-frequency roll-off applied during filtered back-projection. A value of 0.0 applies no additional filtering beyond the ramp filter. Increasing it toward 1.5 progressively smooths the reconstruction at the cost of spatial resolution. For defect detection, start at 0.0 and increase only if noise is visually dominant in the reconstructed volume.

Truncation correction (truncation_correction) is relevant when the object extends beyond the detector field of view. Setting a value greater than 0 applies a correction that partially compensates for the cupping artefact introduced by truncated projections, though it does not fully eliminate it.

Post-Reconstruction Processing​

After reconstruction, a bilateral filter can be applied to reduce noise while preserving edge sharpness:

volume_operations.bilateral_filter([reconstructed_volume], 1, 8000, 2)

The parameters control the spatial filter width and intensity range window. For typical metal or polymer CT data, the defaults above provide a reasonable starting point. The bilateral filter is preferable to Gaussian smoothing here because it does not diffuse high-contrast material boundaries — a property that matters for accurate void boundary delineation in the next stage.

Void / Inclusion Analysis​

With a reconstructed and filtered volume, the void/inclusion analysis detects regions of anomalous intensity relative to the surrounding bulk material.

Voids appear as locally lower-intensity regions (gas pockets, shrinkage cavities, cracks). Inclusions appear as locally higher-intensity regions (denser foreign materials). The mode is selected at analysis creation time and cannot be changed without creating a new analysis object.

Detection Method​

Two detection modes are available:

• Absolute: Applies a fixed intensity contrast threshold relative to the estimated background level.
• Relative: Applies a percentage-based contrast threshold relative to the local intensity range.

For most industrial CT datasets, enabling auto_absolute_contrast is the appropriate starting point. This estimates the background (bulk material) intensity automatically, reducing the need for manual threshold tuning between datasets with different exposure or material properties. Manual override is available when the automatic estimate is unreliable — for example, in datasets with strong beam hardening artefacts.

Filtering Detected Regions​

The filtering step determines which candidate regions are included in the final result. The most consequential parameters are:

Statistics​

The analysis returns both per-defect label statistics and aggregate descriptive statistics. The per-defect table includes volume, centroid coordinates, compactness, sphericity, equivalent diameter, and volume fraction — sufficient for most engineering defect characterisation reports. The aggregate porosity value (void_inclusion_results.porosity) gives the total defect volume as a fraction of the analysed region.

print(f"Total defects: {void_inclusion_results.total_defects_found}")
print(f"Total defect volume (mm³): {void_inclusion_results.total_defect_volume}")
print(f"Porosity: {void_inclusion_results.porosity:.4%}")

Exporting Results​

Results can be written to disk as plain-text statistical summaries:

analysis_operations.write_void_inclusion_results_to_disk(
    void_inclusion_results,
    os.path.join(output_dir, 'void_inclusion.txt')
)

Snapshots of the 3D scene, application window, and individual views can be saved programmatically:

app.save_snapshot_to_disk(api.SnapshotType.View3D, 'C:/Samples/View3D.png')

For structured reporting (PDF reports with 3D scenes, charts, and measurement tables), the interactive UI tools under the Analyze ribbon tab provide report generation features not available through the scripting interface.

Video Tutorial​

The full workflow — reconstruction, void analysis, visualisation, and reporting — is demonstrated in the video below:

Related Resources​

• Void/Inclusion Analysis — GUI Reference
• Cone Beam Reconstruction and Void Analysis Workflow — Scripting Tutorial
• Python Scripting API Overview
• Scripting Quick Reference