Abstract
Automatic assembly systems (AASs), which are key tools for mass production, are increasingly being implemented in today’s industry. To ensure that the quality of a product conforms to the manufacturing specifications, inspection and repair stations are added to AASs. Therefore, a major task for design engineers is to select repair strategies which can optimize the performance of an AAS. Studies the performances of both offline and online repairing strategies. Analyses seven factors which potentially affect the performance of AASs with inspection and repair stations. First, studies the system with a full 27 factorial design of experiment. Next, uses additional levels to gain a deeper insight of the linear or non‐linear effect of each factor. Finally, proposes an approximate predictive equation based on a linear regression technique. Engineers and system designers can use this predictive model to estimate the performance of the system given a combination of levels of each of the five factors studied.
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