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experiments is treated next before presenting the actual experimental design,
methodology and analysis of results.
4.2 Design of experiments
There are a number of ways to design a statistical experimental investigation, out of
which the most commonly used and thorough approach is the full factorial
experiment. There are 2^k possible combinations for any full factorial experiment that
must be tested (where k is the number of factors, each at two levels). Since the
number of factors become too high, it becomes very difficult to carry out
investigations. To reduce the number of tests required, fractional factorial
experiments (FFEs) were developed. Fractional factorial experiments allow only a
portion of the total possible combinations to estimate the main effects of factors and
some of their interactions [31].
The determination of factors to investigate depends upon the product or process
performance characteristics or response of interest. The customer who uses a
product expects or needs some function from a product. If during initial development
stages of a product the function is not provided or consistently provided, the
performance characteristic will have to be improved. Different methods are useful for
determining which factors to include in initial experiments. These may be discussed
as follows [32].
Brainstorming
This involves bringing together a group of people related with particular problems
and soliciting their advice concerning what to investigate. Here it is very appropriate
to bring in product or process experts and statistically oriented people to discuss the
factors and the structure of the experiment.
Flowcharting
Flowcharts are useful in determination of factors affecting the process results. The
flowchart adds some structure to the thought process and thus may avoid the
omission of important factors. All factors that are thought to influence a performance
characteristic should be included in the initial round of experimentation. It is better to
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