2026-09-19T03:27:16Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/353032022-01-13T07:54:36Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Daniel D. Frey.
Li, Xiang, Ph. D. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Dept. of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2007-01-10T15:37:30Z
2007-01-10T15:37:30Z
2006
2006
http://hdl.handle.net/1721.1/35303
75960345
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Page 156 blank.
Includes bibliographical references (p. 151-155).
This 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.
(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.
by Xiang Li.
Ph.D.
156 p.
678639 bytes
678045 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
System regularities in design of experiments and their applications
Thesis
application/pdf
Publication
unknown
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<Abstract>This 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.</Abstract>
<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.</Abstract>
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