Measurement Studies Utilizing Similarity Evaluation between 3D Surface Topography Measurements

Abstract

In the realm of quality assurance, the significance of statistical measurement studies cannot be overstated, particularly when it comes to quantifying the diverse sources of variation in measurement processes. However, the complexity intensifies when addressing 3D topography data. This research introduces an intuitive similarity-based framework tailored for conducting measurement studies on 3D topography data, aiming to precisely quantify distinct sources of variation through the astute application of similarity evaluation techniques. In the proposed framework, we investigate the mean and variance of the similarity between 3D surface topography measurements to reveal the uniformity of the surface topography measurements and statistical reproducibility of the similarity evaluation procedure, respectively. The efficacy of our framework is vividly demonstrated through its application to measurements derived from additive-fabricated specimens. We considered four metal specimens with 20 segmented windows in total. The topography measurements were obtained by three operators using two scanning systems. We find that the repeatability variation of the topography measurements and the reproducibility variation in the measurements induced by operators are relatively smaller compared with the variation in the measurements induced by optical scanners. We also notice that the variation in the surface geometry of different surfaces is much larger in magnitude compared with the repeatability variation in the topography measurements. Our findings are consistent with the physical intuition and previous research. The ensuing experimental studies yield compelling evidence, affirming that our devised methods are adept at providing profound insights into the multifaceted sources of variation inherent in processes utilizing 3D surface topography data. This innovative framework not only showcases its applicability but also underlines its potential to significantly contribute to the field of quality assurance. By offering a systematic approach to measuring and comprehending variation in 3D topography data, it stands poised to become an indispensable tool in diverse quality assurance contexts.