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dc.contributor.advisorDaniel D. Frey.en_US
dc.contributor.authorLi, Xiang, Ph. D. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Mechanical Engineering.en_US
dc.date.accessioned2007-01-10T15:37:30Z
dc.date.available2007-01-10T15:37:30Z
dc.date.copyright2006en_US
dc.date.issued2006en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/35303
dc.descriptionThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionPage 156 blank.en_US
dc.descriptionIncludes bibliographical references (p. 151-155).en_US
dc.description.abstractThis dissertation documents a meta-analysis of 113 data sets from published factorial experiments. The study quantifies regularities observed among main effects and multi-factor interactions. Such regularities are critical to efficient planning and analysis of experiments, and to robust design of engineering systems. Three previously observed properties are analyzed - effect sparsity, hierarchy, and heredity. A new regularity on effect synergism is introduced and shown to be statistically significant. It is shown that a preponderance of active two-factor interaction effects are synergistic, meaning that when main effects are used to increase the system response, the interactions provide an additional increase and that when main effects are used to decrease the response, the interactions generally counteract the main effects. Based on the investigation of system regularities, a new strategy is proposed for evaluating and comparing the effectiveness of robust parameter design methods. A hierarchical probability model is used to capture assumptions about robust design scenarios. A process is presented employing this model to evaluate robust design methods.en_US
dc.description.abstract(cont.) This process is then used to explore three topics of debate in robust design: 1) the relative effectiveness of crossed versus combined arrays; 2) the comparative advantages of signal-to-noise ratios versus response modeling for analysis of crossed arrays; and 3) the use of adaptive versus "one shot" methods for robust design. For the particular scenarios studied, it is shown that crossed arrays are preferred to combined arrays regardless of the criterion used in selection of the combined array. It is shown that when analyzing the data from crossed arrays, signal-to-noise ratios generally provide superior performance; although that response modeling should be used when three-factor interactions are absent. Most significantly, it is shown that using an adaptive inner array design crossed with an orthogonal outer array resulted in far more improvement on average than other alternatives.en_US
dc.description.statementofresponsibilityby Xiang Li.en_US
dc.format.extent156 p.en_US
dc.format.extent678639 bytes
dc.format.extent678045 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectMechanical Engineering.en_US
dc.titleSystem regularities in design of experiments and their applicationsen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.identifier.oclc75960345en_US


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