Characterisation of the topography of metal additive surface features with different measurement technologies

The challenges of measuring the surface topography of metallic surfaces produced by additive manufacturing are investigated. The differences between measurements made using various optical and non-optical technologies, including confocal and focus-variation microscopy, coherence scanning interferometry and x-ray computed tomography, are examined. As opposed to concentrating on differences which may arise through computing surface texture parameters from measured topography datasets, a comparative analysis is performed focussing on investigation of the quality of the topographic reconstruction of a series of surface features. The investigation is carried out by considering the typical surface features of a metal powder-bed fusion process: weld tracks, weld ripples, attached particles and surface recesses. Results show that no single measurement technology provides a completely reliable rendition of the topographic features that characterise the metal powder-bed fusion process. However, through analysis of measurement discrepancies, light can be shed on where instruments are more susceptible to error, and why differences between measurements occur. The results presented in this work increase the understanding of the behaviour and performance of areal topography measurement, and thus promote the development of improved surface characterisation pipelines

Review of the mathematical foundations of data fusion techniques in surface metrology

The recent proliferation of engineered surfaces, including freeform and structured surfaces, is challenging current metrology techniques. Measurement using multiple sensors has been proposed to achieve enhanced benefits, mainly in terms of spatial frequency bandwidth, which a single sensor cannot provide. When using data from different sensors, a process of data fusion is required and there is much active research in this area. In this paper, current data fusion methods and applications are reviewed, with a focus on the mathematical foundations of the subject. Common research questions in the fusion of surface metrology data are raised and potential fusion algorithms are discussed

Knowledge modeling for specifications and verification in areal surface texture

The 25178 series of standards in areal surface texture covers terms and definitions for specification and verification operators and is being developed by work group (WG) 16 in the International Standards Organization (ISO) TC 213. As there are many innovative concepts and definitions included in these standards, it is often considered difficult for mechanical engineers to comprehend and for computing engineers to apply in computing science. This paper presents the utilization of category theory to model sophisticated knowledge in the field of areal surface texture. The ISO 25178 series can be divided into specification and verification series according to the principles of Geometrical Product Specifications (GPS). In the category model, categories and objects are used to represent different knowledge structures; arrows and pullbacks are used to sketch diverse connection between objects; functors are utilized to reveal the structure-preserving mapping between categories in specification and verification. In this paper the function of pullbacks is considered to be a pullback inference mechanism since most of the objects in the model can be determined by different pullbacks. The knowledge model in this paper is the foundation for developing a design and measurement information system in areal surface texture for manufacturing industry

Measurement of thin film interfacial surface roughness by coherence scanning interferometry

All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)Coherence Scanning Interferometry (CSI), which is also referred to as scanning white light interferometry, is a well-established optical method used to measure the surface roughness and topography with sub-nanometer precision. One of the challenges CSI has faced is extracting the interfacial topographies of a thin film assembly, where the thin film layers are deposited on a substrate, and each interface has its own defined roughness. What makes this analysis difficult is that the peaks of the interference signal are too close to each other to be separately identified. The Helical Complex Field (HCF) function is a topographically defined helix modulated by the electrical field reflectance, originally conceived for the measurement of thin film thickness. In this paper, we verify a new technique, which uses a first order Taylor expansion of the HCF function to determine the interfacial topographies at each pixel, so avoiding a heavy computation. The method is demonstrated on the surfaces of Silicon wafers using deposited Silica and Zirconia oxide thin films as test examples. These measurements show a reasonable agreement with those obtained by conventional CSI measurement of the bare Silicon wafer substrates