Browsing by Subject "Process control -- Statistical methods"

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    Monitoring and detecting shifts in the mean in quality levels for a production environment with properties found in the geometric Poissin process
    (Texas Tech University, 1999-05) Chen, Ching-Wen
    For statistical process control, an important property is the underlying statistical model that is assumed to govern the defect generation process. For control of defects, the assumption is made that the defects follow a Poisson distribution. However, frequently the process is more complex and the distributions of defects are more appropriately modeled by the compound Poisson distribution. A defective item may have more than one defect that cause the item to be defective. The occurrence of defective items may follow a Poisson distribution. If an item is defective, the number of defects per item will follow another distribution. In this research, it will be assumed that given an item is defective, the number of defects follows the geometric distribution. Thus, the distribution of defects over time is the Poisson compounded with the geometric. From the viewpoint of quality control, process quality can be improved by moving special causes. Two broad types of special causes, transient and persistent special causes, are reported in the literature. Two proposed methods, the empirical Bayes control chart for the geometric Poisson random variables and the geometric Poisson CUSUM control scheme, aim at removing both transient and persistent special causes. Both proposed approaches utilize the historical information concerning the process. The former can detect shifts in much wider situations. The latter would be more sensitive to small sustained shifts caused by persistent special causes. Using the simulated data, the performance of these proposed quality control methods and classical Poisson-based control methods is compared. The test results show an underestimation of Type I error and the number of false alarms generated, if the underlying defect distribution is wrongly assumed. Although two alternatives in detecting mean shift or structure change for the geometric Poisson random variables are proposed, the relationship between these two is complementary.
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    Productivity control and analysis from the statistical method viewpoint
    (Texas Tech University, 2000-08) Perazzoli, Victor
    In a manufacturing operation data are collected and added a unit of measurement to become information. It is then analyzed through a theoretical framework to become knowledge. Once data has become knowledge, it is used to increase the probability of making good decisions. These decisions allow to predict, and therefore to manage. There are infinite amounts of data that can be collected from a manufacturing operation. But only certain type of data can be useful to help manage and increase the probability of making good decisions. With the use of knowledge, it is decided which of the infinite mass of data are important for use.
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    Statistical process control performance characterization under field conditions
    (Texas Tech University, 1997-05) Karim, Mehmud Zaglul
    Performance characterization of SPC technology is necessary in order to assess the impact of potential SPC strategies and actions. In this research a simulation-based tool (SPClab) is defined, designed and developed that can be used to study SPC options and its performance characteristics, considering both iid and non-iid data streams with and without step shifts in the data stream. The step shifts can be in location and/or in dispersion. The tool has three modules (1) simulation module, (2) performance module and (3) Output/report module and is developed using Borland C++ version 3.1. The number of programming lines needed to complete the tool is 20,019. The tool was tested and demonstrated in the cases of both iid and cyclical response data streams. The results from the simulation agree closely with those of theoretical values and other investigations found in the literature for the normally distributed iid case, indicating the tools validity. The tool, SPClab, was also used to demonstrate how to assess the impact of different SPC strategies and actions by considering different sampling scheme. The adverse consequences of applying SPC models inappropriately to non-iid data streams were illustrated. An appropriate physical-covariate based modeling approach is also introduced