Development of methods for measuring and characterising the surface topography of additively manufactured parts - Newton, Lewis (2020)
Surface texture metrology, concerned with small-scale features present at a surface, is a fundamental tool for the improvement of manufacturing processes. Each manufacturing process produces a surface texture with measurement performed to understand the process and assess functionality and engineering tolerance. Metal powder bed fusion (PBF), a branch of additive manufacturing (AM), uses an energy source to selectively sinter or melt powder in bed layer-by-layer; resulting in a complex surface that presents new challenges for conventional measurement. The PBF surface also creates challenges for finishing operations, which remain essential to produce surfaces that meet functional and tolerancing requirements. The thesis aims to optimise the focus variation microscope (FV), validate feature-based segmentation approaches, apply feature-based characterisation approaches to PBF surfaces and to monitor the evolution of surface topography during the application of finishing operations. Whilst optical measurement techniques are being used to measure AM surfaces, good practice is not clearly defined. Using statistical regression modelling, measurement settings of FV were modelled against metrics of measurement quality: non-measured points, repeatability error and surface texture. The results show significant general trends across all surface types that can be used to inform good practice. Increasing resolution led to reductions in repeatability error, but increased instances of non-measured points. Measurement quality was also improved by using higher intensity illumination types. Feature-based characterisation allows for the segmentation and characterisation individual features, such as particles/spatter, but still requires validation of the accuracy and validity of feature identification. Using x binary classification testing between a manual reference and three algorithmic segmentation approaches (morphological segmentation on edges, contour stability analysis and active contours), metrics for accuracy were used to compare and validate segmentation methods for different PBF test cases. No segmentation approach was best overall, with results highly dependant on the test case and a general trade-off between identifying features and the ability to accurately define feature boundaries. Feature-based characterisation is applied to PBF surfaces, offering an enhanced assessment of the dimensional properties of surface features; only currently applied to top surfaces. Active contours segmentation of the particle/spatter features was applied to surfaces with varying build orientation angle. As the build orientation angle increases, towards facing into the powder bed, there is an increasing proportion of features. The underlying surface, deprived of features, shows a reduction in parameter values when compared to the original surface - a result of removing top heights of the surface. The effect and requirements of finishing operations applied to PBF surfaces needs further understanding and assessment. Using fidicual marksers and alignment and registration in six degrees of freedom, the evolution of surface topography can be monitored after finishing is applied. Linishing, laser polishing and shot peening were shown to all have a different effect on the initial topography in terms of material removal or surface reformation. By comparing surface texture parameters and feature properties of aligned topographies between finishing condition, the changes in heights on the surface in absolute terms and within the same coordinate system is shown. Overall, the work within this thesis contributes to the development of good practice in the measurement and characterisation of additive manufactured surfaces, whilst applying novel characterisation approaches to as-built and finished AM surfaces. For future work, the determination of measurement uncertainty for the measurement and characterisation of additive manufactured surfaces is fundamental.