In the automated inspection of components for surface defects using machine vision techniques, illumination must be tightly controlled in order to obtain images which highlight defective areas. This means that lighting and viewing arrangements tend to be dedicated to a particular task and are not readily adaptable to the more general case where component geometries and surface reflectivities may vary. This thesis describes the design and development of an intelligent robot vision system for the in-process monitoring of complex geometry component surfaces during abrasive polishing. Together with the use of a pair of robot manipulators as the orientating device and a periscopic lighting and viewing arrangement, the system is able to recognise and avoid those conditions which normally give rise to unsuitable images. An important feature of the system concerns the use of a computer object model of component surface geometry to assist in the inspection process. The model is derived from automated component measurements using a novel triangulation technique. The model permits an intelligent selection of viewing geometry and helps to overcome the difficult problem of interpreting images from doubly curved surfaces with non- uniform illumination. To minimize inspection cycle times an off-line optimal selection of views for the inspection of the complete object is required. This information is held in a goal oriented database which contains all the data required during in-process inspection. As the intensity variations within an image are constrained to correlate with the optical and topographical properties of the surface an initial rapid discrimination of surface defects by global grey level thresholding is possible. This is reinforced by a more sophiscated second stage analysis towards the end of the machining cycle. This employs the density of textural edges to determine the acceptability of the surface.
|Date of Award||1982|