Similarity evaluation of 3D surface topography measurements

With the recent advances in three-dimensional (3D) optical scanning technologies, 3D surface topography measurement plays an increasingly important role in many fields, such as product quality inspection in additive manufacturing (AM), gauge capability analysis, and firearm identification in forensic science. In this paper, we establish a thorough and flexible new framework of surface topography data comparison that generates a scaled similarity score for a pair of measurements, and distinguishes matched and non-matched pairs based on the score. If two measurements are portraying the same surface, they are defined as a matched pair. If not, they are defined as a non-matched pair. This similarity evaluation framework can be very useful in comparing optical scanning systems, quantifying different sources of variation in a process, and monitoring the stability and uniformity of a process, thus has a great potential to improve the quality assurance cycle of AM processes in the long run. We illustrate the framework and statistically evaluate the binary classification performance on data measured from additive manufactured parts on a large scale. We also examine how different systems, repeated measurements, and operators affect the similarity score and classification performance. We compare our work with a baseline method using the surface roughness average parameter Ra. The results show that the methodology can distinguish matched and non-matched pairs with high accuracy, and it outperforms the baseline method greatly. The framework serves as a benchmark method and can be generalized to be used in other fields where surface topography plays a critical role.

Automated summary

The study establishes a comprehensive and flexible framework for surface topography data comparison, which generates a similarity score and has the potential to improve the quality assurance cycle in additive manufacturing processes.