Study On Multivariate Process Adjustment Technology For Complex Product Manufacturing

Quality of products is the lifeline of enterprise.It plays a crucial role for onlinequality control technology to improve product quality in the production.Statisticalprocess adjustment is an important means of online quality control techniques that canbe used to compensate the disturbance in the process and decrease process fluctuationonline to improve the quality of product effectively through the process adjustment. Ithas been increasingly widely used and gradually becomes one of the key technologiesto improve process capability and quality of product.With the progress of technology and product manufacturing complexity, it hasbeen the complex interference factor and multiple variables of parameterscharacteristics in the manufacturing process for complex product. Based on existingresearch of statistical process adjustment, multivariable setup adjustment problemsand multivariable autocorrelation process adjustment technology in the complexproduct manufacturing process have been as the research object. Technologies andmethods are presented through the modeling and analysis of different kind ofdisturbance factors in the process, and the estimation and adjustment of the qualitycharacteristics of process output. The optimal adjustment scheme of process ispresented under the condition of considering cost structure of the process.The research can be summarized as follows:Firstly, for the initial bias of multivariable process setup adjustment problems,since the process adjustment may have adjustment error, the setup adjustment problemwith adjustment error based on AR model is studied in the manufacturing of complexproducts. Based on the state-space process control model, the optimal adjustmentscheme in order to minimize the total quality loss of process is derived by Kalmanfilter on line estimation and random process control theory considering cost structureof process. The results of example and simulations demonstrate the effectiveness andfeasibility of the proposed process adjustment policySecondly For the finite horizon multivariate process with setup error, the setupadjustment problem is discussed considering colored observation noises. Based on thestate-space process control model, the optimal adjustment scheme of process isdeveloped by the random linear optimal control theory. A simulation case is presented to illustrate the implementation method of the optimal adjustment policy. The effectsof the optimal adjustment strategies are compared with quality adjustment policy withwhite noise observation noises, which expands the research and application ofstatistical process adjustment technology.Thirdly, considering the correlation between values of multiple qualitycharacteristics in the complex product manufacturing process, the statistical processadjustment problem of multivariate autocorrelation process is studied. The optimaladjustment scheme is developed to minimize the total process quality loss withquadratic adjustment cost and process dynamic noises with AR model. Based on thestate-space function process model, the optimal adjustment scheme is derived byKalman filter on line estimation and linear optimal control theory,balancing the losscaused by the deviation from the target of output quality characteristic of process andadjustment cost. The results of simulations show that the proposed adjustmentsolution is more effective than traditional adjustment strategies.Lastly, based on linear quadratic Gaussian theory, for the finite horizonmultivariate autocorrelation process, the optimal adjustment policy is presentedconsidering the observation noise with the colored noise and the adjustment cost withthe quadratic function. And the effects of process parameters on the adjustment areanalyzed, the results show that the proposed adjustment policy can effectively reducethe quality loss of the process.The adjustment policies proposed in the thesis are adaptable to multivariate setupadjustment problem and multivariate autocorrelation process adjustment technologyin the complex manufacturing process. The research enriches the online qualitycontrol theory, at the same time it has a certain practical guiding significance to ensureproduct quality and reduce production cost.