Nano-Additive Manufacturing and Non-Destructive Testing of Nanocomposites

She, Yulong; Tang, Jie; Wang, Chaoyang; Wang, Zhicheng; Huang, Zhengren; Yang, Yong

doi:10.3390/nano13202741

Cited by 7 publications

(2 citation statements)

References 117 publications

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“…The process operates as shown in Figure 1 . The transmitter array directs the emission of the ultrasound signals from each unit to form the desired acoustic beam [ 25 ]. Simultaneously, the receiver array manages the timing of each unit to capture reflected signals.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

Stable 3D Deep Convolutional Autoencoder Method for Ultrasonic Testing of Defects in Polymer Composites

Liu,

Yu,

Liu

et al. 2024

Polymers

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Ultrasonic testing is widely used for defect detection in polymer composites owing to advantages such as fast processing speed, simple operation, high reliability, and real-time monitoring. However, defect information in ultrasound images is not easily detectable because of the influence of ultrasound echoes and noise. In this study, a stable three-dimensional deep convolutional autoencoder (3D-DCA) was developed to identify defects in polymer composites. Through 3D convolutional operations, it can synchronously learn the spatiotemporal properties of the data volume. Subsequently, the depth receptive field (RF) of the hidden layer in the autoencoder maps the defect information to the original depth location, thereby mitigating the effects of the defect surface and bottom echoes. In addition, a dual-layer encoder was designed to improve the hidden layer visualization results. Consequently, the size, shape, and depth of the defects can be accurately determined. The feasibility of the method was demonstrated through its application to defect detection in carbon-fiber-reinforced polymers.

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Section: Data Collection and Preprocessingmentioning

confidence: 99%

Stable 3D Deep Convolutional Autoencoder Method for Ultrasonic Testing of Defects in Polymer Composites

Liu,

Yu,

Liu

et al. 2024

Polymers

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“…Low structural fidelity not only impedes the complete restoration of details and accuracy in the target model but also affects overall performance [ 4 , 5 ]. Common printing defects, including material unevenness, poor interlayer bonding, cracks, surface roughness and deformation, pose limitations to the widespread adoption and advancement of 3D printing [ 6 , 7 , 8 ]. It is of vital importance to ensure precise material deposition in order to guarantee the structural integrity and functionality of complex hierarchical structures in natural tissues during their complete construction.…”

Section: Introductionmentioning

confidence: 99%

Spatially Resolved Defect Characterization and Fidelity Assessment for Complex and Arbitrary Irregular 3D Printing Based on 3D P-OCT and GCode

Fan,

Yang,

Wang

et al. 2024

Sensors

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To address the challenges associated with achieving high-fidelity printing of complex 3D bionic models, this paper proposes a method for spatially resolved defect characterization and fidelity assessment. This approach is based on 3D printer-associated optical coherence tomography (3D P-OCT) and GCode information. This method generates a defect characterization map by comparing and analyzing the target model map from GCode information and the reconstructed model map from 3D P-OCT. The defect characterization map enables the detection of defects such as material accumulation, filament breakage and under-extrusion within the print path, as well as stringing outside the print path. The defect characterization map is also used for defect visualization, fidelity assessment and filament breakage repair during secondary printing. Finally, the proposed method is validated on different bionic models, printing paths and materials. The fidelity of the multilayer HAP scaffold with gradient spacing increased from 0.8398 to 0.9048 after the repair of filament breakage defects. At the same time, the over-extrusion defects on the nostril and along the high-curvature contours of the nose model were effectively detected. In addition, the finite element analysis results verified that the 60-degree filling model is superior to the 90-degree filling model in terms of mechanical strength, which is consistent with the defect detection results. The results confirm that the proposed method based on 3D P-OCT and GCode can achieve spatially resolved defect characterization and fidelity assessment in situ, facilitating defect visualization and filament breakage repair. Ultimately, this enables high-fidelity printing, encompassing both shape and function.

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Multiscale design and digital light processing preparation of high-strength SiOC ceramic metastructures for tuning microwave absorption properties

Wang,

Tang

et al. 2024

Ceramics International

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Nano-Additive Manufacturing and Non-Destructive Testing of Nanocomposites

Cited by 7 publications

References 117 publications

Stable 3D Deep Convolutional Autoencoder Method for Ultrasonic Testing of Defects in Polymer Composites

Stable 3D Deep Convolutional Autoencoder Method for Ultrasonic Testing of Defects in Polymer Composites

Spatially Resolved Defect Characterization and Fidelity Assessment for Complex and Arbitrary Irregular 3D Printing Based on 3D P-OCT and GCode

Multiscale design and digital light processing preparation of high-strength SiOC ceramic metastructures for tuning microwave absorption properties

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