Efficient Sampling for Computer Vision Inspection in        Automated Quality Control
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Machine vision inspection systems are on the threshold of becoming              commercial.  Their great advantage is that they can be used to check            the geometric quality of parts that are too complex for human                   inspectors.  Their great disadvantage is that they are too slow, partly         because they process vast amounts of data and computation times,                therefore, are too long.  This work will result in ways to get around           this problem, using statistical sampling to reduce, rationally, the             amount of data required to assure part quality.  The scope of the work          is restricted to the basic measurements of straightness and roundness.          The approach is to develop a mathematical relationship between sample           size and inspection accuracy.  Next, a relationship will be established         between sample size and processing time for selected machine vision             systems.  Then criteria will be established for selection of a proper           sampling method.  These will be put into forms easily referenced by             quality assurance practitioners.  Sample size will be stated in terms           of number of scans across the test part, and number of points within            each scan.  Inspection accuracy will be expressed in terms of the size          of the confidence interval (error) of the measured quality                      characteristics.
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{'Code': '5514', 'Text': 'OPERATIONS RESEARCH'}
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