Industrial manufacturing processes can experience a variety of changes to important quality characteristics as a result of tool breakage, tool wear, introduction of new raw materials, and other factors. Statistical process control charts are often used to monitor for changes in quality characteristics for manufacturing processes. The control chart computes a statistic based on measured observations of the process and compares it to control limits. When the statistic exceeds a control limit, the control chart signals that the process is out-of-control. Quality engineers would then search for the special cause responsible for the change in the process. Rapid detection by the control chart is important to minimize the production of poor quality items as a result of an out-of-control process. Control charts that detect changes rapidly can therefore save critical process down-time and expense. This research investigates several new control charts related to the commonly used cumulative sum (CUSUM) control chart. The new control charts use control limits that change as a function of the number of process observations instead of remaining constant. The performances of these new control charts are compared to several other charts including the Shewhart chart, the CUSUM, and the exponentially weighted moving average charts. Compared to the standard CUSUM, the proposed control charts can detect a change in a process more rapidly for a given range of shifts in the mean of a process. The proposed control charts offer a flexibility to quality engineers for better optimization of monitoring schemes for manufacturing processes. / A Thesis submitted to the Department of Industrial Engineering in partial fulfillment of the requirements for the degree of Master of Science. / Spring Semester, 2004. / March 31, 2004. / Average Run Length, ARL, Generalized Sequential Probability Ratio Test, Quality Engineering, Control Charts, CUSUM / Includes bibliographical references. / Joseph J. Pignatiello, Jr., Professor Directing Thesis; Okenwa I. Okoli, Committee Member; James R. Simpson, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_180867 |
| Contributors | McCulloh, Ian (authoraut), Pignatiello, Joseph J. (professor directing thesis), Okoli, Okenwa I. (committee member), Simpson, James R. (committee member), Department of Industrial and Manufacturing Engineering (degree granting department), Florida State University (degree granting institution) |
| Publisher | Florida State University, Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text |
| Format | 1 online resource, computer, application/pdf |
| Rights | This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). The copyright in theses and dissertations completed at Florida State University is held by the students who author them. |
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