Integrated Diagnostic Approach Oriented to Process Output Quality Characteristics and Input Parameters

An integrated quality diagnosis method is proposed to detect the input parameters. It can diagnose both the output quality and input quality in a multiple-input-multiple-output (MIMO) manufacturing process. This integrated diagnosis method overcomes the deficiencies of traditional quality control and diagnosis method that can only diagnosis the output quality of manufacturing process. It can detect the input parameters of the manufacturing process and provide sensitivities analysis results for adjustment of input parameter. The quality out of control situation can be firstly detected by the establishment of residual error T2 control chart. Then, the origin output quality parameters that arouse the process quality anomaly can be found out by BN-MTY approach. It integrated the Bayesian network and MYT theory to estimate the origin output quality parameters through the decomposition of residual error of T2 control chart. Neural network and sensitivity analysis are used in the integrated network to get the weight and threshold value of nerve cell in the forecasting network. They are applied to calculate the sensitivities of input parameters to the root output quality by sensitivities computational formula. 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.