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

Abstract

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.

Efficient Detection of Defective Parts with Acoustic Resonance Testing Using Synthetic Training Data

“…Based on such FEM data, conclusions were drawn algorithmically about unknown defects from a given set of eigenfrequencies [31][32][33]. A simulation-based generation of synthetic ART training data was initially addressed in our previous publication, with an application focus not on defect detection, but on the correlation between the geometries and the eigenfrequencies of undamaged parts (some material, datasets, and essential facts are revisited here, without giving the reference designation each time again to avoid repeated citation) [34].…”

Examining the Feasibility of Acoustic Resonance Testing (ART) for Quality Control in Manufacturing

“…Furthermore, by comparing simulation data and experimental data, it is possible to determine which measurement methods are suitable for determining which eigenfrequencies and which correction factors must be applied [5,6].…”