2026-09-18T19:03:16Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/531262022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Stanley B. Gershwin.
Karamancı, Kaan
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2010-03-25T15:04:32Z
2010-03-25T15:04:32Z
2009
2009
http://hdl.handle.net/1721.1/53126
503457390
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Includes bibliographical references (p. 113).
In this thesis, I proposed and implemented a methodology to perform preemptive quality control on low-tech industrial processes with abundant process data. This involves a 4 stage process which includes understanding the process, interpreting and linking the available process parameter and quality control data, developing an exploratory data toolset and presenting the findings in a visual and easily implementable fashion. In particular, the exploratory data techniques used rely on visual human pattern recognition through data projection and machine learning techniques for clustering. The presentation of finding is achieved via software that visualizes high dimensional data with Chernoff faces. Performance is tested on both simulated and real industry data. The data obtained from a company was not suitable, but suggestions on how to collect suitable data was given.
by Kaan Karamancı.
M.Eng.
113 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Exploratory data analysis for preemptive quality control
Thesis
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