Masters Theses


"Component selection can be very difficult for designers, since there can be, and often are, more solutions than a designer, or even a team of designers can investigate in a practical amount of time. The tools presented here can help designers who wish to use failure analysis as the major ranking criterion to rank solutions. One tool allows for intelligent component selection at the design level by investigating the component’s impact on the overall failure mode distribution. It allows designers to use all failure modes, or to use only those modes pertinent to the specific design. This is accomplished by using outside knowledge from aspects such as operating environment and useful life.

The second tool presented combines techniques that are mutually beneficial to designers into an automated algorithm that selects an optimum component combination for a set of defined functional needs. The program uses an automated concept generator to quickly generate all possible combinations. Unassemblable component sets are eliminated using a Design Structure Matrix. The remaining combinations are then sorted based on likeliness of failure and presented to the designer. The designer can then review the best combinations and compare them with other criteria to make a final selection. A case study is used to measure the usefulness of these two tools. Using the functional model from a student design course requiring the design and construction of an air-powered vehicle, a set of designs was selected by the program to be the least likely to fail. Using these predictions, student designs that were very similar to this design were found to have a lower occurrence of failure during competition"--Abstract, page iv.


McAdams, Daniel A.
Stone, Robert B.

Committee Member(s)

Dharani, Lokeswarappa R.


Mechanical and Aerospace Engineering

Degree Name

M.S. in Mechanical Engineering


This research was supported by NASA grant NCC 2-5490.


University of Missouri--Rolla

Publication Date

Fall 2005

Journal article titles appearing in thesis/dissertation

  • Computational methods to predict and avoid design failure
  • Automated methods for failure avoidance in design


x, 65 pages

Note about bibliography

Includes bibliographical references.


© 2005 Brian Alan Mitchell, All rights reserved.

Document Type

Thesis - Restricted Access

File Type




Subject Headings

Engineering design -- Mathematical models
System failures (Engineering)
Failure Analysis System (Computer system)

Thesis Number

T 8908

Print OCLC #


Link to Catalog Record

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