Integrated Quality Diagnosis Algorithm Method based on Neural Network and Sensitivity Analysis to Input Parameters

Abstract

In order to find out the key input parameters, which aroused the output quality out of control during the  manufacturing process, an integrated quality diagnosis algorithm for input parameters was proposed. The diagnosis method extends the traditional quality control and diagnosis method that only for the output quality of manufacturing process. It can detect the input parameters of the manufacturing process and provide sensitivities of input parameter for adjustment. Firstly, through the establishment of residual error T²control chart, the quality failure situation can be detected. Then, the BN-MTY method was applied to explain the reason of quality failure in T²control chart and the root output quality characteristic that aroused the process quality anomaly was located. The integrated method of neural network and sensitivity analysis was used to get the weight and threshold value of never cell in the forecasting network. They were applied to calculate the sensitivities of input parameters to the root output quality. Sensitivities represent the importance of the input parameters to the output quality failure. This integrated quality diagnosis method can both diagnose the output quality characteristics and the input parameters