Wang, Yan (2008) A knowledge-based intelligent system for surface texture (virtual surf). Doctoral thesis, University of Huddersfield.

The presented thesis documents the investigation and development of the mathematical
foundations for a novel knowledge-based system for surface texture (VitualSurf
system). This is the first time that this type of novel knowledge-based system has been
tried on surface texture knowledge. It is important to realize that surface texture
knowledge, based on new generation Geometrical Product Specification (GPS) system,
are considered to be too theoretical, abstract, complex and over-elaborate. Also it is not
easy for industry to understand and implement them efficiently in a short time.

The VirtualSurf has been developed to link surface function, specification through
manufacture and verification, and provide a universal platform for engineers in
industry, making it easier for them to understand and use the latest surface texture
knowledge. The intelligent knowledge-base should be capable of incorporating
knowledge from multiple sources (standards, books, experts, etc), adding new
knowledge from these sources and still remain a coherent reliable system. In this
research, an object-relationship data model is developed to represent surface texture
knowledge. The object-relationship data model generalises the relational and object
orientated data models. It has both the flexibility of structures for entities and also good
mathematical foundations, based on category theory, that ensures the knowledge-base
remains a coherent and reliable system as new knowledge is added.

This prototype system leaves much potential for further work. Based on the framework
and data models developed in this thesis, the system will be developed into
implemental software, either acting as a good training tool for new and less
experienced engineers or further connecting with other analysis software, CAD
software (design), surface instrument software (measurement) etc, and finally applied
in production industries.

ywangfinalthesis.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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