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A
Simple Application Example
Perhaps the best
way to explore RENO’s features and functionality is by considering some
application examples. The software is shipped with an assortment of
sample files that address reliability, optimization, risk, financial and
other types of analyses. Each file includes documentation on the
analysis process and you can use these sample projects to familiarize
yourself with the software's analytical potential. In addition, a
collection of case study examples are
presented on this Web site.
The simple example presented here has been designed to estimate the
percentage of times that a hinge assembly will be out of specification
based on the known variabilities for its component parts.
Problem Statement
 A hinge is made up of four components. Suppose that the part
dimensions vary as follows:
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A - Normal distribution with mean = 2 and std = .02
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B - Normal distribution with mean = 2 and std = .02
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C - Normal distribution with mean = 30 and std = .2
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D - Normal distribution with mean = 34.5 and std = .5
Determine the expected percentage of the time that (A+B+C) will be
greater than D (i.e. that the hinge assembly will be out of spec).
RENO Solution
The first step to define this model in RENO is to create definitions
for the Random Variables A, B, C and D. This will allow the simulation
to set the width of each component with a randomly generated number
based on the specified distribution and parameters.
The next step is to create a flowchart to model the problem. There
are usually many different ways to model a particular analysis problem.
The flowchart presented here includes four steps executed sequentially
from left to right (based on the direction of the arrows).
The equation defined in the first Standard Block computes the
combined widths of components A, B and C, using values that are randomly
generated based on the variable definitions.
Next, a Conditional Block checks to see if A+B+C is greater than D.
The input from the “Compute A+B+C” Block is passed to the Conditional
Block, which in turn checks against the width of component D (based on
the distribution and parameters defined for Random Variable D). If true,
then it continues the execution by passing a specified value (in this
case, 1) to the next construct in the true path. The false path is not
used for this analysis.
Another Standard Block represents an equation that converts the
results from a count to a percentage. The reserved keyword IN represents
the value passed from the previous construct and SIMS_TOTAL represents
the total number of simulations performed on the flowchart.
A Result Storage construct stores a sum of the results across all
simulations. This represents the estimated percentage of times that
A+B+C was greater than D.
[Click to
Enlarge...]
The final step is to use the Simulation Console to specify the
simulation settings, including the number of simulations to be
performed, and to start the simulation.
When the simulation completes, the storage variable results are
displayed in the Excel®-compatible Simulation Results Explorer and in the
flowchart.
[Click to Enlarge...]
Conclusion
Based on 1,000 simulations, the analysis estimates that A+B+C will
exceed D approximately 16.2% of the time. You can follow a similar
procedure to model and analyze scenarios of interest to you!
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Questions?
