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detected impacts above the user-defined sensitivity
threshold are indicated with a single point on the
Maximum G v. Velocity graph. Points are identified
on the graph with differentiating symbols corresponding
to text labels applied by the user. The graph
also shows one of several produce-specific built-in
damage boundaries and typical response curves for
solid steel and 1/8" (3mm) P20125 padded surfaces
to assist with interpretation. In the graph
shown here, the "Steel" and "P20125"
lines represent expected response on these two surfaces.
In general, impact points that are further to the
left or toward the top of the graph represent more
severe impacts than points that are further toward
the right or toward the bottom of the graph.
No points on this graph are near the "Steel"
line; therefore this particular file had no impacts
on bare, solid metal surfaces. Even small, low G
impacts on bare, unprotected steel can cause severe
damage to many types of produce. This is why
the "Apple" damage boundary is sloped
from the lower left, where a low G impact has causes
10% damage to the upper right where a higher G impact
is required to cause the same amount of damage.
The "Apple" line represents 10% damage
for 5 varieties of apples. Points above this
line will have a greater than 10% likelihood of
causing bruise damage. Points below the Apple
line have less than a 10% likelihood of causing
bruise damage to apples. Notice that the apple
line is not flat. This is because at higher velocities
(softer surfaces) impacts have to be larger (higher
maximum G) to cause the same amount of damage.
Using
this graph and the text labels, the manager would
identify the impacts that are the most serious and
fix those parts of the system first. In this
particular data file, the most serious impacts are
those inside the red square. These points
are all above the damage boundary. Of these,
the most serious impacts are those that are the
furthest from above the damage boundary. After
the equipment has been modified, the IRD should
be run through again to measure the improvement.
"Before" and "After" data sets
may be viewed on one graph to compare results.
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