Surface Roughness Characterization in Additive Manufacturing

Surface roughness characterisation in additive manufacturing addresses the measurement, analysis and prediction of the microscopic texture that arises during layer-by-layer fabrication. The nature of powder-bed fusion, directed energy deposition and material extrusion processes gives rise to unique topographical features such as unmelted particles, layer steps, balling and spatter. These features influence mechanical performance, fatigue resistance, tribological behaviour and post-processing requirements. A range of techniques—contact profilometry, optical interferometry, focus variation microscopy and X-ray computed tomography—has been employed to quantify areal and profile parameters. Complementary analytical and simulation approaches seek to relate process variables (laser power, scan speed, powder characteristics, build orientation) to measurable surface metrics such as average roughness (Sa), root-mean-square height (Sq) and power spectral density. Emerging data-driven methods aim to generate synthetic as-built topographies and to integrate high-fidelity measurements into digital twins for real-time monitoring and control. The global drive towards high-performance aerospace, medical and automotive components underscores the need for robust, standardised surface characterisation workflows that can predict functional behaviour and guide optimisation of additive manufacturing platforms.

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Surface Roughness Characterization in Additive Manufacturing publication trend

The graph below shows the total number of articles in surface roughness characterization in additive manufacturing across all publications each year (not limited to Nature Index journals).

Technical terms

Powder-bed fusion (PBF): An additive manufacturing process in which a thermal energy source selectively fuses regions of a powder bed to build parts layer by layer.

Areal surface texture (Sa, Sq): Quantitative measures of surface roughness over a defined area; Sa is the arithmetic mean of absolute height deviations, while Sq is the root-mean-square height.

Power spectral density (PSD): A representation of surface roughness contributions as a function of spatial frequency, used to distinguish texture features at different scales.

Generative adversarial network (GAN): A machine learning framework comprising two neural networks—a generator and a discriminator—that iteratively improve to produce synthetic data resembling real measurements.

Downskin/Upskin areas: Surface regions in powder-bed fusion defined by their orientation to the build direction; downskin refers to overhangs requiring support, while upskin denotes surfaces facing away from supports.

References

1. Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks. Friction (2023).
2. Measurement of additively manufactured freeform artefacts: The influence of surface texture on measurements carried out with optical techniques. Measurement (2023).
3. Investigation on surface characteristics of wall structures out of stainless steel 316L manufactured by laser powder bed fusion. Progress in Additive Manufacturing (2024).
4. Areal topography measurement of metal additive surfaces using focus variation microscopy. Additive Manufacturing (2019).
5. Areal surface texture data extraction from X-ray computed tomography reconstructions of metal additively manufactured parts. Precision Engineering (2017).
6. Optimization of surface measurement for metal additive manufacturing using coherence scanning interferometry. Optical Engineering (2017).