Similarity evaluation of topography measurement results by different optical metrology technologies for additive manufactured parts

The surface topographic measurements can be used by the additive manufacturing (AM) industry for in-situ quality inspection. However, disagreements may arise when we use different technologies to measure the topography of the same sample surface due to noise, sampling or optical properties of the sample surface, which may cause miscommunications or confusions between manufacturers. Thus, proposing methods for rating the similarities to match surface topographic data measured by various optical techniques is of crucial importance. This research investigates similarity evaluation methods for three-dimensional point-cloud topography data acquired by different technologies. Two different optical techniques (focus variation microscopy and structured light scanning) are used as testbeds. We propose two similarity evaluation methods for three-dimensional point-cloud data based on image distance method and Pearson's correlation coefficient. The experimental results demonstrate that the proposed methods are effective and informative in determining whether the measured data are collected from the same sample, even though the measuring systems have different working principles and resolutions. This research facilitates our understanding of the discrepancies between different measuring systems, and meanwhile benefits a cyber-manufacturing system where unified inspection methods are unavailable among different manufacturers sharing the metrology data in cyber space.