Acoustic resonance testing

Coffey, Ethan

doi:10.1109/fiiw.2012.6378332

Cited by 20 publications

(4 citation statements)

References 1 publication

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“…A quality assessment of manufactured components can be achieved by evaluating the eigenfrequencies or other characteristics derived from the measured natural vibration behavior. Such an approach is referred to in relevant sources, among others, as acoustic resonance testing (ART) or resonance inspection [1][2][3][4][5][6][7]. Related to the research focus of this publication, ART aims at the series-integrated 100% quality screening of metallic or ceramic parts manufactured in large quantities and with short cycle times.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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Analyzing eigenfrequencies by acoustic resonance testing enables a fast screening of components regarding structural defects. The eigenfrequencies of each specific part depend on the general geometric and material properties, including tolerable part-to-part variations, as well as on possible structural flaws. Separating good parts from defective ones is not straightforward and each application-specific sorting algorithm is usually found from experimental training data. However, there are limitations and training data collection may be intricate. We worked on this challenge focusing on machine-made model parts varying slightly in geometry. The application objective was the eigenfrequency-based detection of parts featuring a through-hole test defect drilled into some of the parts and enlarged stepwise. The eigenfrequencies were measured concomitantly. Unlike the industry standard, our approach is based on synthetic training data created mainly by simulation techniques, which resulted in a principally satisfactory classification of the good and defective parts. However, the parts with small defects were not identified from the eigenfrequencies alone, due to overlaying geometric variations. In order to counteract such noise and to improve defect detection based on synthetic training data, the specific actual part geometry was used, in the sense of additional a priori information. A multimodal data evaluation model showed a clearly enhanced sorting power.

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Section: Introductionmentioning

confidence: 99%

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

2022

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“…Hence, given a summary about the matter in Table 1 , apart from gearboxes or bearing systems, to the best of our knowledge, there has actually been no extensive study on sintered cogwheel small data using acoustic resonance testing (ART) [ 17 ] with the help of traditional and modern ML methods in the context of non-destructive testing (NDT).…”

Section: Introductionmentioning

confidence: 99%

Acoustic Resonance Testing of Small Data on Sintered Cogwheels

Kraljevski

Neunübel

et al. 2022

Sensors

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Based on the fact that cogwheels are indispensable parts in manufacturing, we present the acoustic resonance testing (ART) of small data on sintered cogwheels for quality control in the context of non-destructive testing (NDT). Considering the lack of extensive studies on cogwheel data by means of ART in combination with machine learning (ML), we utilize time-frequency domain feature analysis and apply ML algorithms to the obtained feature sets in order to detect damaged samples in two ways: one-class and binary classification. In each case, despite small data, our approach delivers robust performance: All damaged test samples reflecting real-world scenarios are recognized in two one-class classifiers (also called detectors), and one intact test sample is misclassified in binary ones. This shows the usefulness of ML and time-frequency domain feature analysis in ART on a sintered cogwheel dataset.

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“…Acoustic resonance testing (ART), also referred to as resonant or resonance inspection (RI), for example, is a nondestructive inspection method, see References [1][2][3][4][5][6][7]. It evaluates eigenfrequencies or other characteristic values derived from the measured natural vibration behavior of the parts to be inspected.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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.

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Acoustic resonance testing

Cited by 20 publications

References 1 publication

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

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

Acoustic Resonance Testing of Small Data on Sintered Cogwheels

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

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