New Non-Destructive Surface Roughness Inspection of Additively Manufactured Metallic Parts Using Scanning Acoustic Microscopy

Although additive manufacturing enables the production of metallic components with complex geometries, accurately characterizing their surfaces remains challenging, especially in regions beyond the reach of conventional techniques. To address this challenge, this study investigates scanning acoustic microscopy (SAM) as an automated, non-destructive method for measuring the surface roughness of stainless steel 316L cubes fabricated by selective laser melting. A dedicated image-processing pipeline was developed to detect the water–specimen interface in SAM B-scan images using robust surface echo tracking and convert it into a calibrated surface profile. ISO-based spatial filtering was then applied to separate roughness from measurement noise, waviness, and surface form, enabling the calculation of different surface roughness parameters. To evaluate the proposed method, Ra was selected for validation using seven reference standards with nominal values ranging from 0.8 to 50μm. The influence of Gaussian filtering was also investigated by comparing two formulations. SAM successfully distinguished all seven roughness levels, with absolute deviations ranging from 0.327 to 1.772μm for the cutoff-matched formulation and from 0.199 to 4.075μm for the simplified formulation. Furthermore, repeatability measurements demonstrated mean coefficients of variation below 2% across all investigated specimens. Comparison between the proposed SAM pipeline, digital and laser confocal microscopes showed mean absolute deviations of 0.75, 1.30, and 2.85μm, respectively, from shared reference values. Building on this comparison, the optical instruments were used to assess SAM measurements on fabricated cubes without known reference values. Overall, the findings support SAM as a complementary method for quantitative surface roughness evaluation.