Thesis-2000-Millman.pdf (22.19 MB)
Computer vision for yarn quality inspection
thesis
posted on 2018-07-31, 08:25 authored by Michael P. MillmanStructural parameters that determine yarn quality include evenness, hairiness and twist. This
thesis applies machine vision techniques to yarn inspection, to determine these parameters in
a non-contact manner. Due to the increased costs of such a solution over conventional
sensors, the thesis takes a wide look at, and where necessary develops, the potential uses of
machine vision for several key aspects of yarn inspection at both low and high speed
configurations.
Initially, the optimum optical / imaging conditions for yarn imaging are determined by
investigating the various factors which degrade a yarn image. The depth of field requirement
for imaging yarns is analysed, and various solutions are discussed critically including
apodisation, wave front encoding and mechanical guidance. A solution using glass plate
guides is proposed, and tested in prototype. The plates enable the correct hair lengths to be
seen in the image for long hairs, and also prevent damaging effects on the hairiness
definition due to yarn vibration and yarn rotation. The optical system parameters and
resolution limits of the yarn image when using guide plates are derived and optimised.
The thesis then looks at methods of enhancing the yarn image, using various illumination
methods, and incoherent and coherent dark-field imaging. [Continues.]
History
School
- Mechanical, Electrical and Manufacturing Engineering
Publisher
© Michael MillmanPublisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
2000Notes
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy at Loughborough University.Language
- en