Suppression of front and back surface reflections in ultrasonic analytic-signal responses from composites

Zilidou, Maria; Smith, Robert A.; Wilcox, Paul D.

doi:10.1016/j.ultras.2022.106815

Cited by 9 publications

(6 citation statements)

References 45 publications

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“…The signal from the first ply is assumed to occur approximately 40% of a ply thickness after the first wall, and each initial guess for the next ply is assumed to be a full ply thickness after that location. This is consistent with the work of Zilidou et al [36] and Yang et al [35], who identified a rapid mode shape change at the front surface of the part. We found for our specific samples that the use of a 40% reduction in the anticipated thickness for the first ply yielded consistent results of the raw signal.…”

Section: Determination Of Lamina Countsupporting

confidence: 93%

“…It is possible that future works may investigate the use of methods similar to that of Yang et al to align the data prior to analysis, but that is left for future work. This work also differs from the recent work of Zilidou et al [36] in that there is no simulated baseline signal from which to normalize the initial front wall reflection. Based on our studies, it was found that using the negative wave peaks yielded the most accurate results.…”

Section: Determination Of Lamina Countmentioning

confidence: 81%

“…In Figure 2, this occurs at 2.7 µs. We observe that in the initial ∼ 0.3 µs of the signal in Figure 2, the waveform frequency changes considerably; this is due to subtle ringing from the transducer along with the mode shift occurring from the incident 15 MHz initial wave to the natural mode of the part closer to 5 MHz (see, e.g., [36]). The difference in samples between the onset of the waveform and the signal for the back wall provides an approximate thickness for the laminate as (see, e.g., [39])…”

Section: Determination Of Lamina Countmentioning

confidence: 94%

“…For example, Yang et al [35] provide an approach using advanced filtering and reconstruction to provide a better quantification of the individual ply thicknesses. Similarly, Zilidou et al [36] use a simulation to generate the expected signal for a given laminate structure and subtract this signal from the physical signal to better represent changes near the front and back surfaces caused by defects.…”

Section: Introductionmentioning

confidence: 99%

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Automated Characterization of the Ply Stacking Sequence of a Woven Carbon Fiber Composite Using Pulse-Echo Ultrasound

Blackman,

Jack

2023

J. Compos. Sci.

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Carbon fiber composites are a popular design material due to their high specific strength. The directional strength of woven composites can be customized by changing the orientation and sequencing of individual lamina within the ply stack. This allows for the potential of specialized parts designed for specific applications, leading to both performance gains and weight savings. One challenge is the ability to characterize non-destructively the orientations of the individual lamina after the manufacturing process. Current industrial methods used to verify the ply stack are destructive to the part, increasing costs and material waste. This creates the need for a non-destructive technique capable of determining the ply stack, both for quality control and for in-service parts, including when there may be access to just a single side of the composite. This research introduces a procedure to scan a fabricated laminated composite using pulse-echo ultrasound coupled with an automated algorithm to determine the layer-by-layer orientation of the ply stack with a specific focus on woven composites. In this work, 12 unique plain-weave laminates ranging from 3 lamina to 18 lamina thick are studied. The orientations of each stacking sequence are different, with some following standard composite design methodologies and others randomly stacked. The mathematical technique presented in this work correctly characterizes non-destructively the orientation of each individual lamina to within 1° with 73% confidence and to within 3° with 98.3% confidence of the as-manufactured orientation.

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Section: Determination Of Lamina Countsupporting

confidence: 93%

Section: Determination Of Lamina Countmentioning

confidence: 81%

Section: Determination Of Lamina Countmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated Characterization of the Ply Stacking Sequence of a Woven Carbon Fiber Composite Using Pulse-Echo Ultrasound

Blackman,

Jack

2023

J. Compos. Sci.

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“…These components were then combined with a genetic algorithm-optimization support vector machine (GA-SVM) to allow for quantitative testing of near-surface defects. Zilidou et al [17] used the analytic signal and its instantaneous parameters to suppress the front-and back-surface reflections of the ultrasonic echoes through response subtraction and substitution. These methods effectively demonstrate the potential of signal processing to extract useful information regarding near-surface defects.…”

Section: Introductionmentioning

confidence: 99%

Near-Surface Defects Identification of Polyethylene Pipes Based on Synchro-Squeezing Transform and Deep Learning

Chen

Hou

et al. 2023

Applied Sciences

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To conduct the ultrasonic weld inspection of polyethylene pipes, it is necessary to use low-frequency transducers due to the high sound energy attenuation of polyethylene. However, one of the challenges in this process is that the blind zone of the ultrasonic transducer may cover a part of the workpiece being tested. This leads to a situation where if a defect appears near the surface of the workpiece, its signal will be buried by the blind zone signal. This hinders the early identification of defects, which is not favorable in such a scenario. To address this issue, we propose a new approach to detect and locate the near-surface defects. We begin by performing a synchro-squeezing transform on the original A-scan signal to obtain an accurate time-frequency distribution. While successful in detecting and localizing near-surface defects, the method alone fails to identify the specific type of defect directly: a limitation shared with other signal processing methods. Thus, an effective and lightweight defect identification model was established that combines depth-wise separable convolution and an attention mechanism. Finally, the performance of the proposed model was compared and visually analyzed with other models. This paper successfully achieves the detection, localization, and identification of near-surface defects through the synchro-squeezing transform and the defect identification model. The results show that our model can identify both general and near-surface defects with an accuracy of 99.50% while having a model size of only 1.14 MB.

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On the ultrasonic characterization of the stacking sequence of CFRP laminates

Yang

Kersemans

2023

NDT & E International

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Suppression of front and back surface reflections in ultrasonic analytic-signal responses from composites

Cited by 9 publications

References 45 publications

Automated Characterization of the Ply Stacking Sequence of a Woven Carbon Fiber Composite Using Pulse-Echo Ultrasound

Automated Characterization of the Ply Stacking Sequence of a Woven Carbon Fiber Composite Using Pulse-Echo Ultrasound

Near-Surface Defects Identification of Polyethylene Pipes Based on Synchro-Squeezing Transform and Deep Learning

On the ultrasonic characterization of the stacking sequence of CFRP laminates

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