Although the consistency, low cost, and high speed of machine vision systems make them suitable for many areas of manufacturing, there exist challenges for online inspection using machine vision systems due to the presence of variations in lighting, part position/orientation and part finish. In this thesis, a novel adaptive machine vision system based on pixel-by-pixel analysis and a neural network based vision system are presented to solve two challenging industrial inspection problems. Both of the vision systems stem from the idea of adaptive image processing to analyze an image with respect to its local properties.

In the first part of the thesis, a pixel-by-pixel analysis based adaptive vision system is designed for an automotive water pump housing surface inspection problem. This vision system is used to inspect the machined surface of a die-cast part. The defects on this part may include pores, dents and scratches. This problem is challenging for several reasons. First, non-uniform surface finish on the parts produces large brightness variations that must be reduced using a controlled lighting work cell and adaptive camera control. Second, the defects can be subtle and less than lmm in diameter, while the surface is roughly 180mm by llOmm. Third, the surface often includes machining marks that appear similar to defects in the image, but are not considered defects. Finally, due to the manufacturing and fixturing variations, the size and location of the area to be inspected varies considerably, so that a simple fixed mask cannot be used to separate this area from the rest of the image. These challenges have been overcome by developing an adaptive machine vision system. This system includes custom-designed controlled lighting, and several software algorithms for adapting to the variations in surface quality and geometry. The system can detect defects as small as 0.15 mm. It has been tested with over 1,700 images that were collected at the factory. The majority of the defects were pores. These pores were correctly classified in 93% of the cases.

In the second part of the thesis, a neural network-based vision system is developed for an automotive beam clip present/absent inspection problem. In this inspection problem, it is difficult to obtain the theoretical expression for the conditions of 'clip present', 'clip absent' and also for the clip orientation. Furthermore, there exist strong variations in this inspection problem, such as changing lighting conditions, environmental disturbances, clip locations and clip orientations. A CMAC neural network-based vision algorithm is developed to overcome these challenges. The CMAC neural network has the ability to learn fast and is suitable for real-time inspection applications. This vision system is demonstrated to correctly classify 100% of the cases, for the given automotive part images, after being trained for 151 seconds.