In-situ damage monitoring of a SiC/SiC ceramic matrix composite using acoustic emission and digital image correlation

Whitlow, Travis; Jones, Ε. L.; Przybyla, Craig

doi:10.1016/j.compstruct.2016.09.040

Cited by 70 publications

(33 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Acoustic signals have been widely used as a mean to closely monitor damages occurring over time in different systems [13,21]. During the mechanical testing in our research, an AE monitoring equipment was used to collect all AE generated during the internal pressurization test of the tubular samples, as described in [10,11].…”

Section: Datasetmentioning

confidence: 99%

“…During material deterioration, acoustic events in the forms of transient strain waves are emitted [12]. For instance, Whitlow et al [13] studied the acoustic emission signals to propose a new method to detect and localize damage in continuous ceramic fiber reinforced ceramic matrix composites. Furthermore, Appleby et al [14] used AE as one of their non-destructive evaluation methods to assess, quantify damage undergone by ceramic matric composites.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

et al. 2020

View full text Add to dashboard Cite

Prognosis techniques for prediction of remaining useful life (RUL) are of crucial importance to the management of complex systems for they can lead to appropriate maintenance interventions and improvements in reliability. While various data-driven methods have been introduced to predict the remaining useful life (RUL) of machinery systems or batteries, no research has been reported on the remaining useful strength (RUS) prediction of silicon carbide fiber reinforced silicon carbide matrix (SiCf-SiCm) materials with pivotal role in its potential usage as a structural material in nuclear reactors and turbine engines. Knowledge of its degradation process is of the utmost importance to the manufacturers. For this purpose, two approaches based on the machine-learning techniques of random-forest (RF) and convolutional neural network (CNN) are proposed to predict the RUS of SiCf-SiCm using only acoustic emission (AE) signals generated during the material’s stress applying process. Experimental results show that the CNN models achieved better predictive performance than the RF models but the latter with expert-engineered features achieves better prediction for AE signals in the early stage of degradation. Additionally, our results demonstrate that both models can correctly predict the SiCf-SiCm RUS as evaluated by our robust testing method from which the best average root mean square error (RMSE) and Pearson correlation coefficient of 3.55 ksi units and 0.85 were obtained.

show abstract

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Strain gauges provide pointwise measurements and thus to monitor an area and locate damage would require many gauges, which would lead to excessive wiring and instrumentation. Therefore, acoustic emission is a commonly used contact method for detecting the onset of damage [1] and can also be used to triangulate where damage is occurring in specimens [2]. Another approach is monitoring the propagation of Lamb waves from transducers to sensors [3].…”

Section: Introductionmentioning

confidence: 99%

Real-time quantification of damage in structural materials during mechanical testing

et al. 2020

View full text Add to dashboard Cite

A novel methodology is introduced for quantifying the severity of damage created during testing in composite components. The method uses digital image correlation combined with image processing techniques to monitor the rate at which the strain field changes during mechanical tests. The methodology is demonstrated using two distinct experimental datasets, a ceramic matrix composite specimen loaded in tension at high temperature and nine polymer matrix composite specimens containing fibre-waviness defects loaded in bending. The changes in the strain field owing to damage creation are shown to be a more effective indicator that the specimen has reached its proportional limit than using load-extension diagrams. The technique also introduces a new approach to using experimental data for creating maps indicating the spatio-temporal distribution of damage in a component. These maps indicate where damage occurs in a component, and provide information about its morphology and its time of occurrence. This presentation format is both easier and faster to interpret than the raw data which, for some tests, can consist of tens of thousands of images. This methodology has the potential to reduce the time taken to interpret large material test datasets while increasing the amount of knowledge that can be extracted from each test.

show abstract

“…Their investigations are focused on using the DIC technique with the SEM images for three-point bending tests of fiber-reinforced laminates. Due to the brilliant high-temperature behavior of ceramic matrix composites, Whitlow et al [5] conducted comprehensive in-situ health monitoring of SiC/SiC and their service life using the DIC method. They studied the ultimate strength and effects of fiber properties on it.…”

Section: Introductionmentioning

confidence: 99%

Micro-structural Deformation Field Analysis of Aluminum Foam using Finite Element Method and Digital Image Correlation

Akhavan

Anaraki

Malian

et al. 2020

IJE

View full text Add to dashboard Cite

In-situ damage monitoring of a SiC/SiC ceramic matrix composite using acoustic emission and digital image correlation

Cited by 70 publications

References 24 publications

Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

Real-time quantification of damage in structural materials during mechanical testing

Micro-structural Deformation Field Analysis of Aluminum Foam using Finite Element Method and Digital Image Correlation

Contact Info

Product

Resources

About