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Title: Contributions to statistical methods of process monitoring and adjustment
Keywords: Statistical process control, engineering process control, measurement delay, multivariate process adjustment, multivariate control charts, profile mon
Issue Date: 28-Mar-2011
Source: VIJAY KUMAR BUTTE (2011-03-28). Contributions to statistical methods of process monitoring and adjustment. ScholarBank@NUS Repository.
Abstract: Statistical methods of process improvement have found numerous valuable applications in manufacturing and non-manufacturing processes. This thesis contributes to statistical process monitoring and process adjustment methods for quality control and quality improvement of industrial processes. One of the problems associated with process adjustment in product industry is unavailability of in-situ data. The delay in measurement is due to the time taken to measure the process quality characteristic, queue at metrology machines, multistage processes etc. The process adjustment strategies for processes with measurement delay are discussed in Chapter 3. It is crucial to consider the economic aspect of process adjustment such as adjustment costs and off target costs. The bounded and unbounded feedback adjustment methods are proposed. The adjustment schemes as a compromise between increase in process variance and adjustment costs are given. The processes experience various types of disturbances depending on the prevailing production environment. Intermittent process disturbances are one of the commonly experienced types of disturbances. The process operates under stable conditions and is affected intermittently by disturbances. The multivariate process adjustment method under such disturbances is considered in Chapter 4. It is proposed to integrate recursive estimation and the multivariate exponentially weighted moving average control chart. The process is monitored on the multivariate exponentially weighted moving average control chart. Once a shift in process is detected, the shift size is recursively estimated and process is adjusted sequentially. Unlike other multivariate controllers, this method does not actuate the process adjustment every period. Hence, suitable for processes where adjustment at every run is not desirable. Another problem encountered in practice is the uncertainty in process disturbance distribution. The uncertainty may be attributed to several upstream machines, raw material variability, several suppliers and changing process conditions. The process adjustment under uncertain disturbance distribution is considered in Chapter 5. The process adjustment strategies for processes with known and unknown initial state under symmetric and asymmetric off target costs are given. Multivariate process monitoring methods have found several valuable applications in industry. One of the crucial needs of multivariate process monitoring methods is an efficient graphical display of the process. A chart which simultaneously displays the information about individual variables and its multivariate description yet remains easily interpretable. A novel graphical representation of multivariate control charts integrating line and column charts is discussed in Chapter 6. The proposed method efficiently displays the process information and is easier & economical for practical implementation. The proposed graphical display assists in identifying the components of multivariate process that have caused the out of control signal. In some processes it is desirable to monitor the relationship between a response variable and a set of explanatory variables. This relationship is referred to as profile. The profile monitoring control charts based on Fisher?s central and non-central F-distributions are proposed in Chapter 7. The proposed control charts perform better than the existing methods in detection of shift in profile variation and perform competitively in detecting the shift in profile parameters. The run length performances of the proposed charts are obtained analytically and generalized to various cases. The proposed monitoring method is very well-suited for practical implementation.
Appears in Collections:Ph.D Theses (Open)

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