Comparison of methods for management of measurement errors in surface topography measurements

• DOI: 10.1016/j.procir.2023.06.186
• Publication Date: 2023-07-18T16:39:51Z
• Authors: Giacomo Maculotti; Gianfranco Genta; Danilo Quagliotti; Hans N. Hansen; Maurizio Galetto
• Keywords: Machine Learning; Topography; Topography; Surface analysis; Machine learning; Surface Analysis

Surface technology is essential to engineer surface properties by topologically optimised design or machining and finishing treatments. Optical surface topography measuring instruments represent state-of-the-art solution to characterise technological surfaces. Topographies measured by optical instruments are affected by errors (non-measured points and spikes), due to complex interactions between the measurand (the topography) and the instrument, liable of poor measurement quality and biasing characterisation. The literature proposes several approaches to manage measurement errors basing on empirical approaches (thresholding, interpolation) and machine learning modelling. This work compares the methods performances applied to industrially relevant case studies (highly polished and native additive manufacturing surfaces).