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A top-down method is presented and studied for quantifying topographic map height (z) fluctuations directly from measurements on surfaces of interest. Contrary to bottom-up methods used in dimensional metrology, this method does not require knowledge of transfer functions and fluctuations of an instrument. Fluctuations are considered here to be indicative of some kinds of uncertainties. Multiple (n), successive topographic measurements (z = z(x,y)) are made at one location without moving the measurand relative to the measurement instrument. The measured heights (z) at each position (x,y) are analyzed statistically. Fluctuation maps are generated from the calculated variances. Three surfaces were measured with two interferometric measuring microscopes (Bruker ContourGT™ and Zygo NewView™ 7300). These surfaces included an anisotropic, turned surface; an isotropic, sandblasted surface; and an abraded, heterogeneous, multilayer surface having different, complex, multiscale morphologies. In demonstrating the method, it was found that few non-measured points persisted for all 100 measurements at any location. The distributions of uncertainties are similar to those of certain features on topographic maps at the same locations, suggesting that topographic features can augment measurement fluctuations. This was especially observed on the abraded ophthalmic lens; a scratch divides the topographic map into two zones with different uncertainty values. The distributions of fluctuations can be non-Gaussian. Additionally, they can vary between regions within some measurements.

This keynote paper gives an overview of emerging technologies of feature-based characterisation, for surface topographies having features. It is complementary to conventional surface characterisation using texture field parameters. An original concept, the feature spectrum is proposed to organise surfaces, in order to help select the appropriate characterisation for different types of surface topographies and to achieve a more direct relationship between characterisation, manufacturing process and surface function. The keynote paper focuses on fundamentals and the state of the art for feature-based characterisation technologies. Applications of feature-based characterisation are illustrated and discussed. Guidelines for future industrial applications are laid out, and considerations for future challenges are addressed. Surfaces, Micro structure, Feature-based Characterisation.

Segmentation of profile features is mainly described in two standards: ISO 4287 for profile elements, used to calculate RSm and Rc, and ISO 12085 for profile motifs, used to calculate R&W parameters. These methods are commonly criticised for their unstability, although they provide valuable results when used in an adequate context [25]. A new profile segmentation based on watersheds is now introduced in ISO 21920-2 and ISO 16610-45, adapted from areal segmentation [ISO 25178-2, [22]. The areal feature parameters, chap. 3 in Characterisation of Areal Surface Texture, Leach Ed. Springer], with the hope to solve most of the existing problems. However, there are very few comparisons between these methods and almost no recommendations for the configuration of the watershed segmentation. This paper discusses the validity of applying segmentation methods on certain profiles and proposes a validity criterion. It then investigates algorithmic differences between the watershed segmentation and the classical Motifs method, compares the results between the two methods, and suggests guidelines and good practices to obtain significant correlations.

A new approach to the validation of surface texture form removal methods is introduced. A linear algebra technique is presented that obtains total least squares (TLS) model fits for a continuous mathematical surface definition. This model is applicable to both profile and areal form removal, and can be used for a range of form removal models including polynomial and spherical fits. The continuous TLS method enables the creation of mathematically traceable reference pairs suitable for the assessment of form removal algorithms in surface texture analysis software. Multiple example reference pairs are presented and used to assess the performance of four tested surface texture analysis software packages. The results of each software are compared against the mathematical reference, highlighting their strengths and weaknesses.

A novel method for the validation of surface texture filtration software is introduced. Mathematically traceable reference pairs for linear Gaussian filtration are developed, utilising Fourier series surface definitions in conjunction with the frequency dependent transmission characteristic of the linear Gaussian filter. The novel method is demonstrated using a library of reference pairs to validate the performance of five surface texture analysis software packages. Investigations into the effects of different surface properties are made in relation to the deviation of the software-obtained results from the traceable reference values. Analysis of variance tests are used to verify the statistical significance of the results.

International audience; This work studies multiscale analyses and characterizations of surface topographies from the engineering and scientific literature with an emphasis on production engineering research and design. It highlights methods that provide strong correlations between topographies and performance or topographies and processes, and methods that can confidently discriminate topographies that were processed or that perform differently. These methods have commonalities in geometric characterizations at certain scales, which are observable with statistics and measurements. It also develops a semantic and theoretical framework and proposes a new system for organizing and designating multiscale analyses. Finally, future possibilities for multiscale analyses are discussed. (C) 2018 Published by Elsevier Ltd on behalf of CIRP.

International audience; This article presents a methodology based on the use of a surface databank, which aims at assessing the discriminating power of 3D roughness parameters. The presented databank is composed of fourteen different sets of surfaces belonging to two main tribological categories: integrity and functionality. Each set of surface is composed of two classes, called A and B. These classes are obtained by varying process conditions or study parameters. The proposed methodology is first thoroughly described in the general case of comparing two classes of one set of 3D surfaces. Then, sandblasted surfaces are used as an example before applying the methodology to the entire database.

A new method for performance validation of surface texture parameter calculation software is introduced, focussing on field surface texture parameters. Surface height functions are defined mathematically, either using Fourier series or polynomials, and are then input into the surface texture parameter definitions to obtain mathematical parameter values. A series of user-adjustable parametric surface functions are defined that correspond to each surface texture parameter, enabling users to create a variety of surfaces to assess their software whilst still retaining mathematical traceability. This method is expanded to include complex surface textures. Chebyshev polynomials are used to perform numerical calculations of surface texture parameters for a selection of polynomial surface functions. Mathematical reference parameter values are calculated for a series of fifteen predefined surfaces and ten parametric surfaces to assess the performance of the software under test for a given dataset resolution. Assessment of the number of significant figures of the software-obtained values that agree with the reference values is used as a performance metric that enables comparison between different third-party software applications for a given dataset resolution. An assessment of the sampling methods used to create discrete datasets of a mathematical surface function for use with numerical third-party software is performed. Two implementations of surface height sampling are used to create datasets that are input into four third-party surface texture parameter calculation software packages, and the results compared, showing a significant variation in the performance metric values for different sampling methods.

This paper reports on an in-depth analysis of ISO 5436 part 2 type F2 reference software for the calculation of profile surface texture parameters that has been performed on the input, implementation and output results of the reference software developed by the National Physical Laboratory (NPL), the National Institute of Standards and Technology (NIST) and Physikalisch-Technische Bundesanstalt (PTB). Surface texture parameters have been calculated for a selection of 17 test data files obtained from the type F1 reference data sets on offer from NPL and NIST. The surface texture parameter calculation results show some disagreements between the software methods of the National Metrology Institutes. These disagreements have been investigated further, and some potential explanations are given.

Unlike field parameters, where every point on the surface is taken into account in the calculation, the category of feature parameters takes into account only identified features on the surface. Features are considered because they play a significant role in a particular function (for example, large peaks and hills play a functional role as contact zones in mechanical engineering applications; while points located in valleys do not). Significant features are identified by segmentation of the surface and selected by a discrimination method known as pruning. Parameters are then calculated to quantify the characteristics of the selected features.

Chromatic confocal probes are single point optical sensors built around a confocal coaxial setting that use chromatic dispersion and decoding to obtain the surface distance. Such sensors are usually installed on scanning stages of surface texture measuring instruments, roundness instruments or coordinate measuring machines. Chromatic confocal probes can measure on, and through, transparent material, detect several interfaces between materials and, therefore, calculate thickness. The metrological characteristics of chromatic confocal probes are close to those of stylus probes and they are often used as a non-contact substitute on stylus profilometers.

The ability to manipulate materials on smaller and smaller scales has led to an increasing demand for surface metrology solutions that facilitate innovation and help to assure quality. While 2D (x, z) surface characterisation is still very common in industry today, in many cases 3D (x, y, z) characterisation is required in order to evaluate the properties and the quality of a surface. It is very likely that 3D characterisation will become the industrial standard within a decade.

Watershed segmentation and Wolf pruning, as defined in ISO 25178-2, allow the detection of significant features on surfaces and their characterization in terms of dimension, area, volume, curvature, shape or morphology. These new tools provide a robust way to specify functional surfaces.