Nondestructive Evaluation of Additive Manufactured Parts Using Process Compensated Resonance Testing

Livings, Richard; Biedermann, Eric; Wang, Chen; Chung, Thomas; James, Steven; Waller, Jess; Volk, Scott; Krishnan, A. Mohana; Collins, Shane

doi:10.1520/stp162020180111

Cited by 12 publications

(4 citation statements)

References 10 publications

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“…Therefore, the determination of the density has become a key element in assessing the quality of produced AM materials. For this, various techniques are currently applied or under investigation, including ultrasonic methods [23], the Archimedean method, microsectioning, and X-ray micro-computed tomography (µCT). While the first two methods only allow conclusions to be drawn regarding the relative density, microsectioning and X-ray µCT also provide insight into the nature and distribution of defects [21][22][23][24][25] (e.g., LoF, key-hole porosity, and inclusions).…”

Section: A C C E P T E Dmentioning

confidence: 99%

Quality control of AlSi10Mg produced by SLM: Metallography versus CT scans for critical defect size assessment

Romano

Abel

Gumpinger

et al. 2019

Additive Manufacturing

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Section: A C C E P T E Dmentioning

confidence: 99%

Quality control of AlSi10Mg produced by SLM: Metallography versus CT scans for critical defect size assessment

Romano

Abel

Gumpinger

et al. 2019

Additive Manufacturing

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“…ART was also used for analyzing additive lattice structures [14,15]. Swept-sine excitation was used to examine additively manufactured bulk parts, based on their natural vibrations [16], or to evaluate aerospace components, with respect to their metallurgical properties and defects [17]. A further work addressed microstructural variations or anomalies within cast iron test samples with the help of ART [18].…”

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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“…The turbine blades have been tested by Vibrant Corporation (link: www.vibrantndt.com) using the Processed Compensated Resonance Testing (PCRT) technique. [36][37] In this technique, the blades are simply supported (gravitydriven) by three piezoelectric transducers which have a fixed configuration. The transducers have a rounded tip in order to achieve a point-like contact between the transducers and the blades.…”

Section: Case Study On Metallic Turbine Bladesmentioning

confidence: 99%

Integrated interval Mahalanobis classification system for the quality classification of turbine blades based on vibrational data incorporating measurement uncertainty

Cheng

Yaghoubi

Paepegem

et al. 2022

Structural Health Monitoring

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Measurements are not exactly accurate, and measurement errors could lead to a biased trained classifier, and finally to a wrong classification of the parts. This paper extends the recently proposed (Integrated) Mahalanobis Classification System with the concept of Interval Mahalanobis distance (IMD) in order to account for measurement uncertainty. This novel Integrated Interval Mahalanobis Classification System (IIMCS) is applied to an experimental case study of complex shaped metallic turbine blades with various damage types. The turbine blades have been vibrationally tested in a wide frequency range. The IIMCS selects a subset of optimal features that contribute the most to the system under the framework of Binary Particle Swarm Optimization, and determines the optimal decision threshold based on Particle Swarm Optimizer. A Monte Carlo method (MCM) is implemented to account for measurement uncertainty, and as such yields an indicator of reliability, implying the confidence level of the classification results. The obtained results illustrate a high performance of the IIMCS for classifying turbine blades based on vibrational response data with measurement uncertainty.

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Nondestructive Evaluation of Additive Manufactured Parts Using Process Compensated Resonance Testing

Cited by 12 publications

References 10 publications

Quality control of AlSi10Mg produced by SLM: Metallography versus CT scans for critical defect size assessment

Quality control of AlSi10Mg produced by SLM: Metallography versus CT scans for critical defect size assessment

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

Integrated interval Mahalanobis classification system for the quality classification of turbine blades based on vibrational data incorporating measurement uncertainty

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