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Simulation-Based Generation of Representative and Valid Training Data for Acoustic Resonance Testing

: Heinrich, Matthias; Rabe, Ute; Valeske, Bernd

Fulltext urn:nbn:de:0011-n-6022423 (5.3 MByte PDF)
MD5 Fingerprint: 4ee870d9fc45189e6ea4b26001aeb030
Created on: 10.9.2020

Applied Sciences 10 (2020), No.17, Art. 6059, 20 pp.
ISSN: 2076-3417
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Journal Article, Electronic Publication
Fraunhofer IZFP ()
Acoustic Resonance Testing (ART); resonance inspection; eigenfrequency analysis; serial inspection; nondestructive testing; finite element eigenfrequency calculation; simulation-based data generation; synthetic training data; geometric variations; part-to-part variations

Analyzing eigenfrequencies of serial parts by acoustic resonance testing enables an efficient nondestructive assessment of component quality or structural state. Usually, each application is based on experimentally acquired training data, which represent the typical natural vibration behavior of the component type to be inspected. From the training data, suitable test characteristics are identified according to the inspection objective. The experimental collection of training data, which involves selecting and characterizing numerous representing parts, is often associated with a great amount of effort. Instead, this work focuses on a simulation-based generation of synthetic training data. Within an application example, the eigenfrequencies of a set of virtual parts were calculated with FEM as a function of geometry. The resulting simulation values were adapted using empirical correction factors, which were derived from both calculated and measured eigenfrequencies of machine-made reference parts. The simulation-based data were finally used to form linear regression models within a training procedure. These models enabled the precise estimation of geometric dimensions of further machine-made parts using their measured eigenfrequencies as input data. The novel approach, which requires the experimental characterization of only a few real parts, can thus significantly reduce the effort associated with efficient and reliable acoustic resonance testing.