3D blog:
note to the engineer
note to the engineer
3D scanning in additive manufacturing: how to control the accuracy of small batches
How 3D scanning helps assess the quality of metal‑printed parts, identify geometric deviations, and manage the SLM process in small series.
3D scanning in additive manufacturing: how to understand that the process is under control
Additive manufacturing rarely resembles a classic assembly line. Especially when it comes to metal 3D printing: the batches are small, the geometry is complex, the deadlines are short, and the cost of an error is high. Under such conditions, it’s not enough to simply print a part and check a few dimensions with a caliper or a CMM. You need to understand how consistently the process reproduces the geometry from part to part.
In the scientific article “Assessment of Controllability in Additive Manufacturing,” this problem is examined using the example of turbine nozzle guide vane blades made of the heat‑resistant alloy, manufactured using the selective laser melting method. Geometric inspection of the parts was carried out using a RangeVision PRO 3D scanner.
In the scientific article “Assessment of Controllability in Additive Manufacturing,” this problem is examined using the example of turbine nozzle guide vane blades made of the heat‑resistant alloy, manufactured using the selective laser melting method. Geometric inspection of the parts was carried out using a RangeVision PRO 3D scanner.
Why are small series difficult to control?
In mass production, statistics work reliably: the more parts are in the sample, the more reliable the process assessment. However, in additive manufacturing, parts are often produced in batches of 5, 10, or 20. This is a common situation for aviation, energy, and medical components.
The problem is that with a small batch size, standard process reproducibility indicators, such as Cp and Cpk, may provide an overestimated assessment. The process appears stable, although in reality, deviations outside the tolerance limits may occur in subsequent batches.
The problem is that with a small batch size, standard process reproducibility indicators, such as Cp and Cpk, may provide an overestimated assessment. The process appears stable, although in reality, deviations outside the tolerance limits may occur in subsequent batches.
What problem does 3D scanning solve?
The turbine blade has a complex aerodynamic surface. For such a part, it is not just one dimension that is critical, but the shape of the entire working surface: the profile, curvature, local deviations, and the stability of the cross‑sections.
Metal parts with complex shapes after SLM printing: to control such geometry, not individual dimensions are needed, but a complete map of surface deviations.
3D scanning solves several problems at once:
- it obtains a digital copy of the real part;
- allows you to compare the scanned surface with the CAD model;
- shows deviations across the entire geometry, not just at individual points;
- provides an array of measurement data for statistical analysis;
- helps to understand whether the error is repeated from part to part;
- allows you to divide parts into groups with different types of deviations.
Comparing a 3D scan with a CAD model shows where the part’s surface deviates from the nominal geometry.
What the control showed using RangeVision PRO
Six batches of nozzle sections, each containing ten blades, were manufactured. After 3D scanning, the parts were divided into groups based on the nature of the deviations. This made it possible to assess not only the compliance of individual blades with the tolerance but also the stability of the printing process.
For a group of eight blades, the standard calculation of the process capability index yielded a value of Cp = 0.96. However, the adjusted estimate for the small sample size was Cp = 0.76. The difference is about 21%.
The practical implication is simple: with small batches, conventional calculations may create the impression that the process is more stable than it actually is. 3D scanning data helps to see this difference and reduce the risk of defects in subsequent batches.
RangeVision 3D scanners are applicable at various stages of working with additive parts:
For a group of eight blades, the standard calculation of the process capability index yielded a value of Cp = 0.96. However, the adjusted estimate for the small sample size was Cp = 0.76. The difference is about 21%.
The practical implication is simple: with small batches, conventional calculations may create the impression that the process is more stable than it actually is. 3D scanning data helps to see this difference and reduce the risk of defects in subsequent batches.
RangeVision 3D scanners are applicable at various stages of working with additive parts:
- After printing: to check how closely the actual geometry matches the CAD model and to identify warping, shrinkage, or local deviations.
- After removing supports: To ensure that the functional surfaces are not damaged during post‑processing.
- After heat treatment: To assess possible shape changes and residual deformations.
- After mechanical processing: To confirm that the critical areas of the part are within the tolerance.
- When setting up the process: To accumulate data by batch and understand which printing parameters affect the accuracy.
Conclusion
3D scanning in additive manufacturing solves a key problem: it transforms the complex shape of a printed part into precise measurement data. This data is needed not only to verify compliance with the CAD model but also to assess the stability of the process in small batches.
Using turbine blades as an example, it has been shown that RangeVision PRO allows obtaining data for analyzing surface deviations and more accurately assessing the reproducibility of the SLM process. This helps the manufacturer not just to identify defects but to manage print quality before launching subsequent batches.
Using turbine blades as an example, it has been shown that RangeVision PRO allows obtaining data for analyzing surface deviations and more accurately assessing the reproducibility of the SLM process. This helps the manufacturer not just to identify defects but to manage print quality before launching subsequent batches.
Metrological class 3D scanner with blue structured light
- 3 scanning zones
- accuracy up to 24 microns
Based on the publication © 2026 Samara National Research University named after Academician S.P. Korolev
A.I. Khaimovich, V.G. Smelov, V.P. Alekseev, V.V. Kokareva "Assessment of controllability by additive manufacturing using effective unbiased estimates of reproducibility indices for small batches with a limited batch size"
DOI: 10.37313/1990-5378-2026-28-1-103-107, EDN: JDZNRC