“Identifying a Minimal Set of Significant Characteristics from Functional Responses in Engineering Design: a Case Study"

January 9, 2007
A Presentation by Ellen Barnes

One common issue in industrial statistics is that the response of interest is a function versus a univariate
response. The key is to find a way of reducing the function to a minimal set of statistics, where the
statistics each have engineering significance. This presentation illustrated an example of reducing the
output from a new test to characterize driver air bag performance into a meaningful, minimal set of
statistics. The process involved a piecewise non-linear fit of the data. The first portion of the fit utilized
a modification of the classic Weibull function, while the second portion involved a quarter of an ellipse.
This presentation included alternatives considered and the criteria that were used to select these two
functions to model the output, and the methodology used to develop the new component specifications
based on the test and the modeled results. The use of the new test and the associated specifications is
streamlining product development by reducing the amount of rework loops in driver air bag development.

Ellen Barnes is a Technical Expert at Ford Motor Company, with a specialty in statistical applications for
design and development of automobiles. She has an M.S. in Applied Statistics degree from Oakland
University (1992), a B.S. in Mechanical Engineering from Columbia University in New York (1976) and
a B.A. in Mathematics and Physics from Grinnell College in Grinnell Iowa (1975). Ms. Barnes has 18
years diverse statistical experience at Ford, and an additional 10 years engineering experience at other
companies. She is a long-time member and the current Secretary of the Detroit Chapter, and has
previously served as Chapter President.




Cecilia Yee giving Ellen Barnesa Certificate of Appreciation from the Detroit Chapter