On-line optimization of quality in a manufacturing system

One of the major obstacles contributing to the cost, time and efficiency of improving the quality output of manufacturing systems is the propagation o f defectives or errors through the system. Design of Experiments (DoE), the response surface plot and a Neural Network Metamodel (NNM) can be used aut omatically to detect the interrelationship of the system without the need f or complex analytical tools and costly intervention. A case study is conduc ted here to demonstrate the capability of DoE, the response surface plot an d NNM in building a decision-support model for achieving six-sigma quality for a manufacturing system with a significant shift in the mean number of d efectives 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 imp lemented on the NNM can be used as an on-line decision support tool in the manufacturing system with a real-time database system.