When we say that a process is "controlled" we mean that we know as much as it is possible to know about that process. When we say that a process has been "characterized", we mean that we know exactly how many critical steps there are, and that we know exactly what happens in each of those steps. Every step has a metric and a tolerance. When that metric is monitored and measured, preferably on a real-time basis, manufacturing management has the necessary data to control that process, and to reap the benefits that come with such control. Developing and installing the instrumentation needed to measure processes required the better part of the 20th Century. Today's process instrumentation can measure every conceivable physical parameter, record it, and communicate it anywhere in the world. This method does have its drawbacks: such instrumentation can be expensive, and often the parameters being measured are only indirectly related to the process. The benefits, however, of a stable, reliable process are worth the effort. The technology that supports instrumentation continually improves, as indeed, it has to if it is not to become obsolete in short order. For some time, however, the greatest improvement in the field has been in improving measurement of physical parameters based on physics, engineering and computer science. However better that focus becomes, process instrumentation development now has an alternative path. Although the end hardware, of course, is still physical, the path is based on a different model: biology, rather than physics. This new path emulates the neural network--that is, the brain. Using neural networks for process control is as old as processes themselves. Long before today's sophisticated instruments, a person would examine a process for some time, learn how it worked, and make a judgement about how well it was actually doing what it was supposed to do. Decisions about process performance were based on what someone observed, and recommendations for improvements were based on that person's experience. Instrumentation is now available that makes judgements in exactly the sme way a person would, but with the precision and tireless reliability of modern electronic equipment. The first of these instruments is called ZiCAM.
The highest barriers to wide scale implementation of vision systems have been cost. This is closely followed by the level of difficulty of putting a complete imaging system together. As anyone who has every been in the position of creating a vision system knows, the various bits and pieces supplied by the many vendors are not under any type of standardization control. In short, unless you are an expert in imaging, electrical interfacing, computers, digital signal processing, and high speed storage techniques, you will likely spend more money trying to do it yourself rather than to buy the exceedingly expensive systems available. Another alternative is making headway into the imaging market however. The growing investment in highly integrated CMOS based imagers is addressing both the cost and the system integration difficulties. This paper discusses the benefits gained from CMOS based imaging, and how these benefits are already being applied.
Until the present date, free flight spark ranges have used conventional film technology for recording the position-attitude histories of projectiles as they traversed the instrumented ranges. Film solutions provided precise position-attitude data, but not without limitations. Film requires substantial range set-up, processing, and analysis time. Days or weeks could pass before a given experiment would have its data reduced for interpolation. Such time delay, and its attendant inefficiencies, could be greatly reduced by using a CCD camera of proper resolution operating in conjunction with a compatible data collection system. This capability would create an electronic shadowgraph system. The purpose of this paper is to describe the camera and data collection system first tested at the free-flight range at Eglin Air Force Base in February of 1992.
United Technologies Adaptive Optics Associates (AOA) in conjunction with EG&G Reticon has developed a 500 Hz, 256 X 256 Pixel Camera System with a PC/AT controller and memory card. This high frame rate camera, the MC4256, is a turn-key solution to a multitude of high-speed imaging problems, applicable to medical imaging, machine vision, and real time inspection and analysis for manufacturing processes. The MC4256 system can control and store up to 1024 frames of 8 bit data.