The book offers a thorough introduction to machine vision. It is organized in two parts. The first part covers the image acquisition, which is the crucial component of most automated visual inspection systems. All important methods are described in great detail and are presented with a reasoned structure. The second part deals with the modeling and processing of image signals and pays particular regard to methods, which are relevant for automated visual inspection.
Smart Inspection Systems: Techniques and Applications of Intelligent Vision will enable engineers to understand the various stages of automated visual inspection (AVI) and how artificial intelligence can be incorporated into each stage to create "smart" inspection systems. The book contains many examples that illustrate and explain the application of conventional and artificial intelligence techniques in AVI. The text covers the whole AVI process, from illumination, image enhancement, segmentation and feature extraction, through to classification, and includes case studies of implemented AVI systems as well as reviews of commercially available inspection systems. Each chapter concludes with exercises. This book will be of interest to users and developers of commercial industrial inspection systems as well as researchers in the fields of machine vision, artificial intelligence and advanced manufacturing engineering.
This work on the multi-disciplinary subject of machine vision offers an introduction to its fundamental principles, covering the interaction of robot vision modules with programming languages, current technical tools in industrial systems and 3-D imaging and early visual processing.
A great deal of research is being done in the areas of artificial vision and neural networks. Although much of this research has been theoretical in nature, many of the techniques developed through these efforts are now mature enough for use in practical applications. Automated Visual Inspection Using Artificial Neural Networks explains the application of recently emerging technology in the areas of artificial vision and neural networks to automated visual inspection. The information is organised in a clear, informative manner, bridging the gap between theoretical research and practical application. Significantly this book includes: * broad coverage of all aspects of the automated visual inspection problem, * details of the HAVENET neural network and the CAMERA vision model, and * detailed descriptions of practical applications of intelligent visual inspection.
In recent years, extensive research and development have applied computer vision to industrial automation. Advances in computer technology, sensing devices, pattern recognition, and image processing techniques have complimented computer vision development. Applications range from simple optical gaging to complex vision-controlled robots. This research concerns the development of automatic visual inspection techniques for printed circuit boards (PCBs). The major goals are to study the problems of programmable inspection systems and to develop a flexible and versatile system that will perform inspection on different products with minimum system modifications.
Currently, manual visual inspection is the gold standard for the required visual inspection of particulate matter in parenteral medicines. Automated visual inspection machines offer an opportunity for Amgen to improve efficiency, rate and consistency, while reducing its equipment footprint. However, the implementation of automated visual inspection poses challenges that need to be resolved. This thesis identified and developed solutions to three execution pain points: (1) low detection rates of dense particles in products; (2) misuse of automated inspection machines for product impact testing; and (3) ambiguous understanding of cost drivers when selecting an inspection method. The pain points mentioned above were addressed separately. First, experiments with modified plunger surfaces were conducted to determine their effectiveness at agitating dense particles into solution where the particles could then be easily detected. Second, embedded sensors were identified as the sensor of choice to measure the mechanical stress history of products passing through an automated visual inspection machine. Experiments were designed to test the effectiveness of accelerometers to replace the limited range of gyroscopes' rotational velocity measurements. Third, a cost benefit analysis model was created that used discounted cash flows to calculate the net present cost of selecting automated visual inspection or manual visual inspection. The results of these three work streams were promising. First, the experiments with modified plunger surfaces showed up to a 97% success rate of agitating particles into solution compared to an 1% success rate for the original plunger design. Second, experiments on accelerometers in embedded sensors showed that the accelerometers could measure centripetal acceleration that related to rotational velocity. A linear regression model was developed to relate accelerometer readings to rotational velocity within an accuracy of 50 RPM. Lastly, the cost benefit analysis model confirmed expected drivers regarding the favorability of different inspection methods. The model also showed that automated visual inspection is the cheaper method of inspection, even with conservative estimates of cost of capital and false eject rates. A follow-up effort is necessary to achieve a more streamlined implementation of automated visual inspection machines throughout Amgen's manufacturing network.
