Research in Engineering Education Symposium 2011 - rees2009

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limited space available, we focus this discussion on aspects of the solution that (i) address
the research questions and (ii) will stimulate fruitful discussion and feedback at REES.
I. The Expert FPM Solution
The expert ChE approached the Virtual CVD Project by first performing a literature review
to identify which fundamental principles could be applied to the process and to find
typical input parameter values. This activity is shown in the box labeled “information
gathering” in Figure 1a. He also used information from the literature to bound the design
space, identifying potential combinations of input parameters that would result in
unfavorable results. Based on this information, the ChE developed several first principles
qualitative and quantitative models to help him understand the process. These models can
be seen along the upper solution path line in figure 1a and they culminated in a single
analytical model constructed of several FPMs shown in the dotted oval in Figure 1a. He
then used this analytical model to predict the input parameter values for his first run,
denoted by the square in Figure 1a, which signifies a model directed run. The expert
performed seven more experimental runs during his solution. He used runs 2-4 to
qualitatively verify his model and to further develop it (as illustrated by 5 new models). He
fine tuned his input parameter values in runs 5-8 and then submitted the final recipe.
II. The Expert SED Solution
The expert ME immediately framed the Virtual CVD Project as an SED problem, and
searched 8 sources from the literature for suitable ranges of input parameter values (see
the ‘Information Gathering’ box). This information led to the development of a Taguchi
L18 experimental design model and complimentary signal to noise ratio model. The oval
shown in Figure 1b shows these as the first models operationalized by the ME and one of
the team’s only primary models. The Taguchi method was used to set parameter values for
18 experimental runs in order to test the effects of six input parameters at three levels (a
full factorial design would require 729 runs). The Model Map shows 13 additional
‘secondary models,’ not on the primary solution path line. These models were used by the
ME to attempt to understand interactions between and relative effects of the input
parameters. They were based on first principles but were developed in a qualitative and
fragmented fashion which revealed a lack of fundamental domain expertise. For example,
he had difficulty determining which of the many equations identified in the literature
review were applicable and how they might be applied. Once the ME had designed his
experiment using Taguchi methods and defined the ranges of input parameters, he
conducted all 18 experiments in the design without developing further models (shown by
the triangle and “1-18” in figure 1b). He then took the best performing run and used the
model generated by Taguchi methods to improve the reactors performance in two
subsequent ‘fine tuning’ runs. The expert ME voiced his desire to run another designed
experiment but was constrained by time and submitted his ‘final recipe’.
Discussion
As predicted, each expert utilized the approach that corresponded to his expertise. As
compared to student teams who pursue FPM or SED approaches, the experts enacted their
Proceedings of Research in Engineering Education Symposium 2011
Madrid, 4 th - 7 th October 2011