Printed-part porosity
Porosity fraction with lack-of-fusion versus gas-pore classification from a cross-section.
Example outputs shown for illustration. Numbers depend on your samples and protocol.
Image: Illustrative rendering (AI-generated), not a micrograph
Results
Each measurement below comes from your own images, tied back to the annotated frame it came from, so results stay comparable across samples, locations, and conditions.
% area
Porosity %
Segmented area of the feature divided by the eligible area in the two-dimensional image.
Example, not a claimed result
In your report: Per-field value, size distribution, and the segmentation overlay used.
% area
Pore count
Segmented area of the feature divided by the eligible area in the two-dimensional image.
Example, not a claimed result
In your report: Per-field value, size distribution, and the segmentation overlay used.
class
Lack-of-fusion vs gas class
Each detected feature assigned to a class using the agreed rule set, shown on the image.
Example, not a claimed result
In your report: Per-feature class, counts by class, and annotated overlays.
% area
Pore size distribution
Segmented area of the feature divided by the eligible area in the two-dimensional image.
In your report: Per-field value, size distribution, and the segmentation overlay used.
per area
Location map
Findings placed in sample coordinates to reveal spatial patterns and local density.
In your report: Coordinate-referenced map, regional density, and representative images.
Built around your image set
Use the calibrated images your team already collects, together with the locations you need to compare.
Each report includes
The artifacts your team receives, ready for review and archive.
Annotated image set
Original images with regions of interest, measurement points, masks, and finding overlays.
Measurement table
Location-indexed values, units, distributions, and QC flags in a structured export.
Method record
Calibration evidence, analysis settings, version history, and validation summary.
Confidence in every resultTraceable measurements, reviewed against your agreed reference method.
- Traceable scale
- The report records the image scale, calibration evidence, and a method-specific expanded measurement uncertainty. It does not use one product-wide accuracy number.
- Reference comparison
- The configured method is compared with your accepted reference method or reviewed annotations, and reports bias by measurement range and image condition.
- Detection performance
- Detections are evaluated against reviewed reference regions with precision, recall, and segmentation overlap, separated by the conditions that affect performance.
- Repeatability
- The locked protocol is rerun on the same inputs and on a defined repeat set. The review records variation from image acquisition, sampling, and analysis separately where possible.
What this result does not establish: Classifies pores by shape from a 2D cross-section: rounded as gas, irregular as lack-of-fusion. A single plane samples the part; confirm bulk porosity by CT or Archimedes.
The measurement, today
Porosity is often a single density number from an Archimedes measurement. It says how much, never what kind or where, and the two pore types have different causes and fixes.
What it costs
Gas pores point at the powder; lack-of-fusion points at the laser parameters. Reporting only a percentage leaves engineers guessing which knob to turn.
From image to reviewed result
- 1
Image the section
Load a polished cross-section or a CT slice of the printed part.
- 2
Threshold the pores
Pores are separated from solid metal by adaptive thresholding.
- 3
Classify by shape
Rounded pores are labelled gas; irregular pores are labelled lack-of-fusion.
- 4
Report and map
Porosity fraction, pore size distribution, and a location map are exported.
Measuring a coating instead?
Use the Paint & Coatings workflow for layer thickness and coating porosity.
Printed-part porosity separates gas pores from lack-of-fusion features. Paint and coating cross-sections need a different protocol: trace the layer boundaries, measure thickness, segment pores inside the coating, and export the review overlays.
Explore Paint & CoatingsNot sure this is the right measurement?
Send a representative image and your measurement goal. A ConductVision scientist will confirm whether this is the right fit, or point you to the closer workflow, before you commit to a quote.
Related applications
Paint & coating analysis
Measure paint and coating thickness, porosity, coverage, and review-ready overlays from calibrated images.
Additive manufacturing QC
From powder morphology to post-build cross-sections: layer anomalies and pore maps.
Metal powder feedstock QC
Particle size, sphericity, and satellite fraction for metal AM feedstock.
Send a sample image and a measurement goal
We will show the closest ConductVision workflow and flag what needs custom validation for your images.
