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The development of numerically controlled machines, group technology, cellular manufacturing and just-in-time (JIT) production systems have revolutionized the way products are designed and manufactured. These technological and strategic advances have changed the role of human operators in the manufacturing environment. The highly specialized work force of the low-tech manufacturing system has evolved into the multi-skilled work force of the high- tech manufacturing system. Among the multiple tasks that an operator is expected to perform in advance manufacturing systems (AMS) are job scheduling, inventory planning, machine set-up, problem-solving and quality inspection.
Throughout this evolution, human sensory detection capabilities have been a vital but often ignored component of the quality inspection task. Although automation is often employed to construct and assemble products within AMS, most inspections and quality checks are still done by human operators due to the inherent problems in machine vision and decision-making. While humans remain responsible for inspection, it has been widely accepted that the quality inspection task performed by humans is prone to error. Some studies indicate human inspectors typically find only ~80% of the defects. Despite the contributions of human factors research to the understanding of human performance in the quality inspection task, the manufacturing trend has been to design quality schemes that compensate for poor inspector performance instead of trying to improve it (Drury 1992).