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Case Studies (Pride Knuckle Arm - Renault Drive Line)  
ARI offers rapid and inexpensive method of 100%  
inspection of parts. ARI can be used to detect  
structural defects such as cracks, cold  
shuts, inclusions, porosity, nodularity  
hardness, density, and hardening  
defects. ARI principle of opera-  
tion is based on analyzing res-  
onant frequencies of parts.  
Every part has a unique resonant  
signature and any deviation from  
the expected signature indicates the  
presence of a flaw. To excite the  
structure and cause it to vibrate an  
impact hammer is used. The part vibrations  
can be measured by two microphones or two  
accelerometers. A single measurement can detect  
defects at any location throughout the part. Advanced  
signal processing methods are used to detect natural  
frequency peaks and their damping factors with a resolution  
down to 0.2 Hz. ARI is a relative test method, which requires a database  
of known parts. During calibration or learning, a database of reference parts including  
good and different defective parts is made.
Typically,  the database can be set up with 100 to 500 good parts from different
production batches.  Various defective  parts  can be optionally included in the
database as well. ARI generates a “decision module” based on advanced statisti-
cal methods and classification algorithms to sort the parts. “Good” parts have
consistent spectral signatures, meaning that their resonant frequencies are the
same among the reference part; while the resonances of “Fail” parts are different
For example, a crack in a part causes certain resonances displaced to low
frequencies or split up into two frequencies; whilst others remain the same.
With hardness testing, “all” resonances are changed due to a hardness deviation
in a part. The decision module checks multiple criteria, each one follows a
particular “logic” to separate defective parts. These criteria analyze the accep-
tance range of resonant frequencies,the mutual relation between two frequencies
the relationship between multiple frequencies, and the classification result
between Pass/Fail groups. A clustering algorithm is applied to compensate for
acceptable process variations. A large variety of data viewing tools are available
to compare the test results of a new part with the statistical features of the
reference parts in the database. We provide customized fixtures for part loading
using (silicon) rubber supprts, automatic part loading/unloading with pneumatic
control, and optionally an isolated enclosure for environmental noise cancelling.
Email : info@rad-ravesh.com
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