Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.765480
Title: Feature-based characterisation of surface topography
Author: Senin, Nicola
ISNI:       0000 0004 7660 7116
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Restricted access.
Access from Institution:
Abstract:
In recent years surface metrology has undergone a revolution: non-contact technologies have driven the transition from profile to areal measurement, and topography in- formation can now be obtained in the form of three-dimensional geometric models. However, the conceptual approach underlying topography characterisation has not changed as much, and surfaces are still predominantly quantified in terms of "rough- ness". This thesis explores feature-based characterisation, an approach that merges technologies from computer vision, image processing, geometric modelling, and statistical modelling, to forge a new set of tools for the analysis of three-dimensional surface topography. Feature-based characterisation provides the end-user with the capability of identifying, isolating and characterising any topographic formation of interest which may be found on a measured surface, addressing characterisation needs that may go well beyond the mere assessment of surface roughness. Feature-based characterisation of surface topography offers new ways to approach cur- rently challenging metrology problems, and offers new opportunities to explore original pathways in the development of advanced manufacturing processes, materials and products. This thesis illustrates original methods developed by the candidate for feature-based characterisation, and presents a first attempt at unifying such methods into a comprehensive framework where feature-based characterisation is seen as an alternative to conventional characterisation based on quantifying roughness. Throughout the thesis, the foundational elements of feature-based characterisation framework will be illustrated and discussed with the help of examples from real-life applications.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.765480  DOI: Not available
Keywords: TS Manufactures
Share: