Computer vision for yarn quality inspection
thesisposted on 31.07.2018 by Michael P. Millman
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
Structural 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.]
- Mechanical, Electrical and Manufacturing Engineering