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Thesis (Ph. D.)--University of Hong Kong, 1987.

Thesis (M.S.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; contains xi, 154 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 112-122).

The work presented in this thesis concerns the development of vision based systems for two hardness (destructive) tests, namely; the Shore and Vickers and a quality assurance non-destructive test. In each case the vision system is based on an IBM PC compatible computer fitted with a commercially available frame store. Bespoke image analysis software was written using the C language for each system. In the Shore test, hardness is judged by the maximum rebound height attained by an indenter incident on a test sample. The purpose of the vision system is to measure the rebound height automatically. Laser light is used to illuminate the indenter and a vidicon vision camera is used to view its motion. Two approaches to the problem are considered; one in which image data is analysed in real time and one in which image·data is merely stored in real time and analysed a posteriori. Non-real time analysis is shown to be superior to real time analysis in terms of accuracy and reliablity and its software implementation is discussed in detail. The Vickers test uses the size of the permanent impression left by an indenter forced into the test material under a known load as a hardness index. In this case the purpose of the vision system is to measure the size of the indentation automatically. The original image analysis algorithms are shown to be capable of analysing good quality samples but are unreliable when applied to poor quality specimens. Further, fault-tolerant, algorithms are described to provide reliable and accurate results over wide variations in sample quality.The quality assurance application involves automated visual inspection of novel ferrite components for defects. Each component is approximately 8 mm in diameter, annular in shape, and coated with aluminium. Laser light is used to illuminate individual components which arc viewed using a charge-coupled device (CCD) video camera. Image analysis algorithms for characterising defects in component geometry and surface finish arc discussed. The system is shown to capable of measuring component edge eccentricity and hole offset as well as providing a quantitative description of surface chips and cracks. The system is further shown to be capable of separately classifying surface defects extending to the edge of a component. Calculation of shape parameters for surface defects also provides a means of distinguishing cracks from surface chips.