Abstract
One of the major obstacles contributing to the cost, time and efficiency of improving the quality output of manufacturing systems is the propagation of defectives or errors through the system. Design of Experiments (DoE), the response surface plot and a Neural Network Metamodel (NNM) can be used automatically to detect the interrelationship of the system without the need for complex analytical tools and costly intervention. A case study is conducted here to demonstrate the capability of DoE, the response surface plot and NNM in building a decisionsupport model for achieving six-sigma quality for a manufacturing system with a significant shift in the mean number of defectives produced. The case study is based on a discrete event simulation model of an actual manufacturing system. A response surface plot is used as an off-line decision support tool. Alternatively, a grid search method implemented on the NNM can be used as an on-line decision support tool in the manufacturing system with a real-time database system.
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