Influence of the Processes Parameters on the Properties of The ...

Chapter 3.
Analytical Methods and Designs <strong>of</strong> Experiments
Linear Graph: <strong>the</strong>y are used to verify that <strong>the</strong> selected table is equipped to represent <strong>the</strong> problem
(can you represent all factors and all interactions) and specify how to assign <strong>the</strong> columns <strong>of</strong> <strong>the</strong>
Taguchi’ table.
Interaction Table: it allows to precise in which columns are <strong>the</strong> influent interactions.
When a non linearity <strong>of</strong> <strong>the</strong> response is assumed, <strong>the</strong>re are tables <strong>of</strong> experiments configured
according to a finite number <strong>of</strong> experiments. For example, <strong>the</strong> chosen table L 9 (3 4 ) need to perform nine
experiments to study 4 factors at 3 levels. In Taguchi’ notation, it is important to note that <strong>the</strong> three levels do
not correspond to <strong>the</strong> American notation (-1, 0, 1) but an increasing variation (1, 2, 3). By consulting tables
which respect <strong>the</strong> principle <strong>of</strong> orthogonallity, which means that all factors vary evenly on each level (cf.
Table 3.3).
Table 3.3: Table Taguchi L 9 .
Experiment Nr 1 2 3 4
1 1 1 1 1
2 1 2 2 2
3 1 3 3 3
4 2 1 2 3
5 2 2 3 1
6 2 3 1 2
7 3 1 3 2
8 3 2 1 3
9 3 3 2 1
To determine <strong>the</strong> effect <strong>of</strong> one factor, we calculate <strong>the</strong> average response <strong>of</strong> each factor in a given
level (e.g. a 1 corresponds to <strong>the</strong> demi-difference between <strong>the</strong> average responses when <strong>the</strong> A factor is at <strong>the</strong>
up and down levels) and plot <strong>the</strong> evolution <strong>of</strong> this factor when it goes from low (1) to high (3). The variation
factor between <strong>the</strong> extreme levels 1-3 will be aware <strong>of</strong> <strong>the</strong> influence factor (little variation for a neglectable
factor) and to compare <strong>the</strong> effects <strong>of</strong> each factor.
Similarly, interactions between factors are obtained by calculating <strong>the</strong> system response when <strong>the</strong>
factors are in certain levels (for example a 1 b 1 it counts only <strong>the</strong> values corresponding to low levels <strong>of</strong> two
factors A and B). By convention, <strong>the</strong> interaction effects are taken equal to half <strong>the</strong> difference between <strong>the</strong>
two effects.
8 Conclusion
The purpose <strong>of</strong> this chapter was to describe <strong>the</strong> analytical methods that are available for detecting,
and/or measuring, and/or monitoring surface, physical and mechanical properties <strong>of</strong> <strong>the</strong> polymers and foams.
The main intention was to identify well-established methods that are used as standard methods <strong>of</strong> analysis.
The glass transition temperature <strong>of</strong> <strong>the</strong> materials used is determined by <strong>the</strong> DSC principle, which fur<strong>the</strong>r
gives results about <strong>the</strong> physical and chemical nature <strong>of</strong> <strong>the</strong> polymeric material. Granulometery gives particle
morphology <strong>of</strong> polymer, fillers and modifier used. Brazilian test is a parameter to calculate <strong>the</strong> structural
aspects <strong>of</strong> <strong>the</strong> raw material. Molecular weight <strong>of</strong> <strong>the</strong> polymer is determined by taking into account intrinsic
viscosity. Scanning electron microscopy, image analysis, mercury porosimetry, microtomography,
compression test provides information about <strong>the</strong> morphology and structural properties <strong>of</strong> <strong>the</strong> end product.
Experimental designs were used with <strong>the</strong> aim <strong>of</strong> minimizing number <strong>of</strong> experiments when we are confronted
to a great number <strong>of</strong> variables. The experiment, based on a statistic plan that was inferred from research,
must be repeatable.
- 86 -