Surface topography analysis software validation

This thesis presents work in the field of surface texture analysis, focussing on improving the current state-of-the-art regarding the calculation of areal surface texture parameters by software. The work given in this thesis utilises the mathematical definitions of surface texture parameters to develop mathematically-defined reference values against which third-parties can compare, to assess the performance of their software. An in-depth analysis of ISO 5436-2 type F2 reference software for the calculation of profile surface texture parameters is 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). Algorithm flowcharts are developed for reference software on offer from the three national metrology institutes to understand the structure of each software package and highlight differences between them. Surface texture parameters are calculated for a selection of seventeen test data files obtained from the type F1 reference data sets on offer from NPL and NIST. The results show differing results across the three reference software packages for a variety of test datasets, and attempts are made to explain these differences from the standpoint of software implementation and the interpretation of the definitions given in ISO 25178-2. The investigation concludes that the use of different software implementations contributed to the differences in obtained parameter values. Results of an international survey are presented, detailing the use of surface texture parameters in industry. The survey received 179 responses from a total of 34 countries, revealing the use of a variety of parameters from ISO 4287, ISO 12085, ISO 13565-2/3 and ISO 25178-2. The survey responses show an increase in the number of users of profile parameters and an increase in the range of surface texture parameters used, compared to the results from a similar survey in 1999, as well as a significant uptake of the newer areal surface texture parameters. Individual sector usage is also discussed. A programmable software framework is developed for the user-defined creation of test surfaces. The framework supports the bespoke design of surfaces using a variety of user-configurable functions and utilises a Fourier series approach to produce mathematically-defined surface functions alongside discrete height-map datasets in accordance with ISO 25178-71. In addition, the software introduces the use of multi-scale Fourier space Gaussian blur to incorporate optional pseudo-realistic random elements into the mathematical surface function. A new method for performance validation of surface texture parameter calculation software is introduced, focussing on functional surface texture parameters. Material ratio curves are defined algebraically and used to calculate functional surface texture parameters mathematically. Discrete datasets are created from the material ratio curves and input into three third-party parameter calculation software packages. Comparisons are made between the software-obtained parameter values and the mathematical values. Work is carried out to highlight inaccuracies introduced by sampling discrete datasets from mathematical representations. 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 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. Chebyshev polynomials are used to perform high accuracy numerical calculations of surface texture parameters for a selection of complex polynomial surface functions. The 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. Two methods of surface texture parameter software performance assessment are presented that utilise the mathematically-traceable surface-parameter reference pairs. Extrapolation of parameter values calculated from a series of increasing resolution datasets is performed to account for the effect of discretisation error on software-obtained results. In addition, an assessment of the number of significant figures of the software-obtained values that agree with the reference values is used as a simple performance metric that enables easy 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