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https://www.scribd.com/doc/203867184/DeGarmo-s-Materials-Processes-Manufacturing
-10th-editionhttps://www.scribd.com/doc/203867184/DeGarmo-s-Materials-ProcessesManufacturing-10th-editionIn order for a test to be accurate, the conditions mos
t be reproduced
that the part will see in the field. This is the source of a huge amount of
the cost involved in conducting testing. The field testing is often not
practical on a large scale. There are companies such as AMES research
that provide the automotive industry with fleet testing services that
drive instrumented automobiles through a wide-range of driving conditions
all over the country. This is a powerful and useful research tool,
but it does take considerable time and effort. Even though the fleet testing
is very close to normal service conditions, it is not exact. The cars
are instrumented, driving continuously by shifts of drivers and being
constantly inspected and repaired.
Testing that is done in the laboratory is done under simulated conditions.
The simulation, by its nature has some error in it. Consider
temperature. Most chambers are thermocouple-controlled. Thermocouples
are typically accurate to 2 4 °C. The chamber itself will have a
temperature variation (properly run laboratories map and document the
temperature variations within the chamber) of several degrees in small
chambers and some times much more in larger chambers. This means
that a test protocol that calls for simulating an 80 °C condition can
be off by several degrees due to the thermocouple. If there are several
samples in the chamber, the individual samples may see temperatures
differing from 2 20 degrees or more depending on the size of the chamber,
the circulation in the chamber, the heat load from the test parts,
the quality of the insulation in the chamber and several other factors.The criti
cal part of this example for the purposes of this book is the
detection methods. Notice that for each potential failure, there is an
effect, a mechanism and a corresponding method of detection. The
method of detection should reflect the suspected mechanism and take
advantage of the potential effect to design a test that will impose the
mechanism and monitor for the effect. For example, the loss of rubber
due to impact can be tested by imparting an impact and monitoring for
rubber loss. Notice that a DFMEA could lead to a very large number
of discrete tests. Just in a brief look at one failure in one design feature
results in four tests. Two of the tests are relatively quick (FEA model,
load testing), and two of them could take a significant amount of time
(thermal cycle, chemical exposure).
This is one of the big downfalls of the DFMEA if it is conducted properly,
it will result in a very exhaustive list of discrete testing.An accelerated reli
ability test can be very repeatable for the defined
conditions. The repeatability of the test can be reduced if more then
one failure mechanism is involved or the time-to-failure is highly sensitive
to small changes in stress. Chambers and vibration tables have
known tolerances of operation. It is critical that the control tolerance
of the equipment be compared to the acceleration curve that is being
measured, and the uncertainty of the resulting curve be considered
when evaluating the accuracy of the results. See ISO Guide 17025 for
requirements on uncertainty calculations.
Limitations
The biggest limitation for this test is the indeterminate amount of time
necessary to execute the test. Because the most accurate results require
testing to failure, the time necessary can vary widely. Running the
samples for a fixed period of time can result in a more defined test, but
may reduce accuracy.
Sample size is also a significant limitation. Sample sizes of three sets of
eight (24 parts) or more are common. In addition, if the extrapolation
to service conditions yields a tight margin relative to a target, more
samples and runs may be needed in order to verify the initial results.

One stress source results in two or three coefficients in the equations.
More than one stress results in exponentially more coefficients.

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