Page 117 - Using ANSYS for Finite Element Analysis Dynamic, Probabilistic, Design and Heat Transfer Analysis

104 • using ansys for finite eLement anaLysis
In a bar chart the most important random input variable (with the
highest sensitivity) appears in the leftmost position and the others fol-
low to the right in the order of their importance. A bar chart describes
the sensitivities in an absolute fashion (taking the signs into account);
a positive sensitivity indicates that increasing the value of the random
input variable increases the value of the random output parameter for
which the sensitivities are plotted. Likewise, a negative sensitivity indi-
cates that increasing the random input variable value reduces the ran-
dom output parameter value. In a pie chart, sensitivities are relative to
each other.
In a pie chart the most important random input variable (with the
highest sensitivity) will appear first after the 12 o’clock position, and the
others follow in clockwise direction in the order of their importance.
Using a sensitivity plot, you can answer the important questions.
How can I make the component more reliable or improve its quality? If
the results for the reliability or failure probability of the component do
not reach the expected levels, or if the scatter of an output parameter is
too wide and therefore not robust enough for a quality product, then you
should make changes to the important input variables first. Modifying an
input variable that is insignificant would be waste of time.
Of course you are not in control of all random input parameters.
A typical example where you have very limited means of control are mate-
rial properties. For example, if it turns out that the environmental tempera-
ture (outdoor) is the most important input parameter then there is probably
nothing you can do. However, even if you find out that the reliability or
quality of your product is driven by parameters that you cannot control,
this has importance—it is likely that you have a fundamental flaw in your
product design! You should watch for influential parameters like these.
If the input variable you want to tackle is a geometry-related param-
eter or a geometric tolerance, then improving the reliability and quality
of your product means that it might be necessary to change to a more
accurate manufacturing process or use a more accurate manufacturing
machine. If it is a material property, then there is might be nothing you
can do about it. However, if you only had a few measurements for a mate-
rial property and consequently used only a rough guess about its scatter
and the material property turns out to be an important driver of product
reliability and quality, then it makes sense to collect more raw data. In this
way, the results of a probabilistic analysis can help you spend your money
where it makes the most sense—in areas that affect the reliability and
quality of your products the most.