Quality Assurance using X-ray computed tomography is made easy and transparent by Dragonfly. Industrial users benefit from non-destructive insights into their parts that can highlight manufacturing errors or unwanted defects such as voids, porosity, cracks, local wall thickness deviations, out-of-range deviations from design and much more.

Image source: Inspection of wall thickness. Data courtesy of Dandong Huari Science Electric Co. Ltd.

Porosity/ Void analysis

Porosity analysis is widely used in industry and this can be performed quickly and easily in Dragonfly. Segmentations can be done using fast and easy automated thresholding tools, and calculations for each pore can be done for pore size analysis with color coding for visualization.


Porosity in a casting analyzed by Dragonfly with color coding by volume
Voids in a ball-grid array analyzed by Dragonfly with color coding by spherical diameter

Wall thickness analysis

Local wall thickness analysis is a popular quality assurance method, useful for highlighting thin parts or checking wall thickness against specifications. Color coding and statistical analysis are also possible.

A local wall thickness analysis shows variations in part thickness using color coding.
A deviation analysis using Dragonfly’s mesh deviation analysis tool highlights deviations from design.

 Deviation from design

Often it is useful to compare a manufactured part to its design file. Dragonfly allows import of the design mesh file (STL) and comparison of this file with the actual part. Color coding can highlight the largest deviations or warping.


Combinations of quality assurance tools are often useful. In addition to the above approaches and combinations of them, other tools often used in industrial quality assurance and R&D laboratories include: particle size distribution analysis, inclusion analysis, mesh export, dimensional measurements, part or feature count, defect distance from surface of object, and much more.

One of the important aspects of quality assurance with Dragonfly, is that inspection tasks can be automated. An example is the segmentation and measurement of battery overhang distances performed by deep learning and the automated analysis and reporting tools in the Battery Insights product.

A 3D view of an impeller with surface metallic rendering (left), porosity visualization in transparent region (middle) and wall thickness analysis (right). Data courtesy of Comet Yxlon.
A combination of wall thickness, porosity analysis and surface rendering for a casting part.
Fibers and composite materials fiber angle analysis with color coding

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