Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/4980
Title: Statistical analysis of deterministic textures in steel sheet production
Authors: Porrino, Alessandro
Advisors: Vermeulen, M
Keywords: On-line process control system;Texturing rolls;Textured sheets
Issue Date: 2004
Publisher: Brunel University School of Engineering and Design PhD Theses
Abstract: Textured surfaces are universally adopted in the steel sheet production industry, and manufacturers are continuously improving the quality of the finished products through intense research in the surface characterisation field. Deterministic Surfaces are textured with specifically designed rolls in order to present a certain degree of regularity, which allows better control over the functional behaviour of the metal sheets. The regularity of the texture impressed on the steel sheets also allows unconventional approaches to surface characterisation and to the assessment of the texture's structure. Statistical analysis is the most effective way to target the isolation of the deterministic part of the surface, which represents the desired product, from the stochastic part, called ‘noise’ and associated with the inaccuracies of production and measurement. This work addresses the problem of characterisation of deterministic textures through statistical analysis, proposing innovative filtering techniques aimed at the realisation of an On-line Process Control System. Firstly the techniques proposed are theoretically formulated and studied, addressing in particular the physical meaning of the geometrical parameters extracted through statistical analysis of highly correlated portions of the textures. A method for isolating the deterministic textures present on a surface, called the Statistical Surface Filter, is presented and discussed in detail, and tested on existing laboratory samples. Secondly the filter is applied to preliminary measurements acquired by an innovative on-line measurement system currently under development, and evidence is shown that the technique is effective in separating the information regarding the regular patterns from the stochastic noise. The possible applications to on-line Statistical Process Control are discussed. Thirdly, the Statistical Surface Filter is tested on a set of measurements representing texturing rolls and textured sheets with different characteristics; statistical analysis of the surface parameters extracted from the filtered surfaces show that the technique allows the assessment of the different contributions of the various stages of the texturing process to the final product. Finally, a software package is implemented for the practical application of the filtering techniques and the parameters extraction; the algorithms that perform the statistical filtering are described and discussed, concluding with the operations of optimisation and fine-tuning for production-line implementation.
Description: This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 24/06/2004.
URI: http://bura.brunel.ac.uk/handle/2438/4980
Appears in Collections:Design
Dept of Design Theses

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