工业工程与管理

Neural Network Control Chart Architecture for Monitoring Non-Conformities in a Poisson Process

Abstract

Yousef S Alhammadi and Michael Adams B

The uses of Neural Network (NN) models have recently been recommended as statistical quality control (SQC) tools. The advantages of NNs, particularly the robustness of the nonlinear modeling abilities, are appealing to quality control practitioners for use in process monitoring. Advances in computing power have also made the Neural Network Control Charts (NNCC) an alternative SQC technique.The systematic Design of Experiment (DOE) methodology is employed to find near optimal NN topology for NNCC for Poisson data. A (2k) full factorial design is implemented and supplemented as needed to investigate NN topologies. The effect of the following factors were investigated through a simulation study: the number of the inputs “n”, the number of nodes in the hidden layer(s), the training data size, and in-control mean for shift range 0-3σ . The guidelines and steps of constructing the DOE study for the NNCC is given, along with an example.