Study On Statistical Process Adjustment For Complex Product Manufacturing

Quality has been given attention more than ever before since 21st century. Statistical process adjustment is one kind of online quality control technologies that can be used to reduce the process variation online to improve the quality of products, mainly by adjusting the level of control factors. It is therefore also one of the necessary technologies of process improvement and quality assurance. With the increasing degree of complexity of products, the manufacturing processes of complex products (system) have shown many features that raise higher requirements for the research and application of statistical process adjustment, such as no pilot or little pilot study, online adjustment easily influenced by so many factors, multiple quality characteristics, and so on.Based on existing research of statistical process adjustment, more embedded study of the online quality control of the discrete manufacturing process of complex products (system) is conducted and presented. Technologies and methods are provided through the modelling and analysis of different kind of processes, and the estimation, control of the characteristics of process output quality. Cost structure is taken into account in setting up the adjustment strategies in order to reduce the overall cost and ensure the quality of the manufacturing process of complex products (system) at the same time.This research can be summarized as follows:Firstly, the adjustment problem for autocorrelation process with unknown parameters is discussed. In practice, since there is very little data from pilot study, the process parameters are too difficult to obtain. By building the state space model for the process, and estimating the unknown parameters by using Bayesian method, given the situations of the adjustment that has a cost or not, the adjustment strategy to minimize the total process lost based on sequencial Monte Carlo method is provided.Secondly, the setup adjustment problem with adjustment error is discussed given that in the manufacturing of complex products (system), short-run processes are becoming more prevalent due to high-degree customization, the adjustment of the process with initial offset is very important, and the adjustment action may also have random error. Since the adjustment has a cost, we trade off the loss caused by the deviation from target of the quality characteristic and the adjustment cost. Based on the deduced predictive distribution of the quality characteristic, the deadband form adjustment strategy is proposed. In this adjustment strategy, the adjustment limits are time-varying, and depending on the stage of process and the process variance. Simulation results demonstrate the feasibility and effectiveness of the proposed adjustment strategy.Lastly, considering that the quality of end products needs to be described by several characteristics in many manufacturing processes of complex products (system), the multivariate statistical process adjustment problem is discussed. We analyze the problem under the known or unknown process parameters respectively. Given fixed adjustment cost and the loss caused by the deviation from target to the quality characteristics, the adjustment strategy is presented respectively. In the situation of known process parameters, the sensitivity of the adjustment strategy with respect to the process parameters and adjustment cost are investigated. In the situation of unknown process parameters, the sensitivity of the adjustment strategy with respect to the number of process stage and prior information are also investigated. With comparison studies, it could be seen that both presented adjustment strategies can achieve good adjusment performance as well as reduce lost effectively in the process.The adjustment strategies for different processes and with different cost structure proposed in the thesis are adaptable to multiple conditions, such as process with unknown parameters, adjustment with random error, and multiple quality characteristics. This research enriches the theory of the statistical process adjustment, and provides guidance to enhance the quality of products, minimize quality loss in the manufacturing of complex products (system).