is found to be appropriate for this experimental
design. S/N is shown in Eq. (1).
(1)
In Eq. (1), n is the number of repetitions for an
experimental combination, i is a numerator, and yi is
the performance value of the ith experiment [17].
Generally speaking, the application of the Taguchi
method leads to economy in cost and time by
decreasing the number of experiments.
Contrary to the Taguchi approach, the full factorial
design considers all possible combinations of a given
set of factors. Since most of the industrial
experiments usually involve a significant number of
factors, a full factorial design results in a large
number of experiments [18]. The response surface
methodology, a collection of mathematical and
statistical techniques, is useful for the modeling and
analysis of problems in which a response of interest
is influenced by several factors. If the response is
modeled by a linear function of the independent
factors, then the approximating function is the firstorder
model Eq. (2).