A Through-Transmission Ultrasonic Method for the Detection of Ferrite Tile Defects
Source abstract
Abstract
A through-transmission ultrasonic method is proposed to address limitations in conventional ultrasonic reflection methods for non-destructive testing of ferrite tiles. The method utilizes a dual-probe configuration on both sides of the test piece to measure ultrasonic transmission signals, overcoming issues related to blind zones and orientation limitations in pulse-echo reflection methods. This method demonstrates excellent capabilities for full inspection of internal and external defects in ferrite tiles. Physical field finite element simulations were conducted to analyze detection capabilities and a transmissive testing system is developed based on the simulation results. Experimental validation was performed on artificially manufactured quantified defect samples in aluminum alloy, and the same testing system was applied to evaluate ferrite tile samples. The results confirmed the effectiveness of the system in distinguishing defective (NG) signals from normal defect-free (OK) signals, with a recall rate of at least 95% on samples of various sizes up to 0.1 mm. This research provides insights for quality control and defect detection technology in ferrite tiles.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Non-Destructive Testing Techniques · Geophysical Methods and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China
Funding
Tsinghua University Shenzhen International Graduate School Research Start-up Fund · Tsinghua University Shenzhen International Graduate School Interdisciplinary Research and Innovation Fund
References (30)
Show source-provided references
1. FFCNN: A Deep Neural Network for Surface Defect Detection of Magnetic Tile10.1109/TIE.2020.2982115 ↗
2. Automatic classification of magnetic tiles internal defects based on acoustic resonance analysis10.1016/j.ymssp.2015.02.018 ↗
3. Multimodal Fusion Convolutional Neural Network With Cross-Attention Mechanism for Internal Defect Detection of Magnetic Tile10.1109/ACCESS.2022.3180725 ↗
4. Wei, J., Zhu, P., Qian, X., and Zhu, S. (2019, January 9–10). One-stage object detection networks for inspecting the surface defects of magnetic tiles. Proceedings of the 2019 IEEE International Conference on Imaging Systems and Techniques (IST), Abu Dhabi, United Arab Emirates.10.1109/IST48021.2019.9010098 ↗
5. Cao, X., Yao, B., Chen, B., and Wang, Y. (2020, January 6–8). Multi-defect detection for magnetic tile based on SE-U-Net. Proceedings of the 2020 IEEE International Symposium on Product Compliance Engineering-Asia (ISPCE-CN), Chongqing, China.10.1109/ISPCE-CN51288.2020.9321855 ↗
6. Internal defect inspection in magnetic tile by using acoustic resonance technology10.1016/j.jsv.2016.07.020 ↗
7. Online recognition of magnetic tile defects based on UPM-DenseNet10.1016/j.mtcomm.2021.103105 ↗
8. Thermal Sprayed Lead-Free Piezoelectric Ceramic Coatings for Ultrasonic Structural Health Monitoring10.1109/TUFFC.2022.3176488 ↗
9. Ozsoy, U., Koyunlu, G., Ugweje, O.C., and Dayyabu, A. (2017, January 28–29). Nondestructive testing of concrete using ultrasonic wave propagation. Proceedings of the 2017 13th International Conference on Electronics, Computer and Computation (ICECCO), Abuja, Nigeria.10.1109/ICECCO.2017.8333326 ↗
10. Mondal, S., and Datta, D. (2019, January 15–17). Ultrasonic Monitoring of Hardness of Industrial Rubbers. Proceedings of the 2019 8th International Conference on Modeling Simulation and Applied Optimization (ICMSAO), Manama, Bahrain.10.1109/ICMSAO.2019.8880357 ↗
11. Liu, M. (2022, January 4–6). Ultrasonic Inspection And Defect Identification of Thin—Walled Stainless Steel Welded Pipe Weld. Proceedings of the 2022 9th International Forum on Electrical Engineering and Automation (IFEEA), Zhuhai, China.10.1109/IFEEA57288.2022.10037864 ↗
12. Cheng, Z., Zhu, K., Li, X., and Qian, X. (2021, January 22–23). Detection of Internal Defects Based on Empirical Mode Decomposition and Wavelets Denoising. Proceedings of the 2021 IEEE Industrial Electronics and Applications Conference (IEACon), Penang, Malaysia.10.1109/IEACon51066.2021.9654506 ↗
13. Flaw identification from time and frequency features of ultrasonic waveforms10.1109/58.503719 ↗
14. Automated Defect Detection From Ultrasonic Images Using Deep Learning10.1109/TUFFC.2021.3081750 ↗
15. Li, K., Wu, Y., Guo, D., Que, K., and Chen, B. (2011, January 24–27). A new method of ultrasonic featured-scan for composite materials. Proceedings of the 2011 International Symposium on Applications of Ferroelectrics (ISAF/PFM) and 2011 International Symposium on Piezoresponse Force Microscopy and Nanoscale Phenomena in Polar Materials, Vancouver, BC, Canada.
16. Defect detection of composite honeycomb structure by vibration analysis technique10.1016/j.matpr.2019.12.192 ↗
17. Automatic detection and characterisation of the first P- and S-wave pulse in rocks using ultrasonic transmission method10.1016/j.enggeo.2020.105474 ↗
18. Comparisons of second- and third-order ultrasonic nonlinearity param-eters measured using through-transmission and pulse-echo methods10.1016/j.ndteint.2022.102757 ↗
19. Defect detection and size estimation in billet from profile of time-of-flight using ultrasonic transmission method with linear scanning10.7567/JJAP.54.07HC11 ↗
20. Study on ultrasonic attenuation characteristics in air-droplet two-phase flow10.1016/j.anucene.2023.109742 ↗
21. Liu, H., Tan, C., and Dong, F. (2022, January 16–19). Improved Amplitude Extraction Method for Attenuation Reconstruction of Transmissive Ultrasonic Tomography. Proceedings of the 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Ottawa, ON, Canada.10.1109/I2MTC48687.2022.9806506 ↗
22. Wang, H.B. (2014). Study on Key Techniques of Ultrasonic Nondestructive Testing Composite Parts. [Ph.D. Thesis, Beijing Institute of Technology].
23. Computation of Acoustic Scattering by Rigid Particles by the Boundary Element Method
24. Huang, D.-H., Liao, C.-K., Wei, C.-W., and Li, P.-C. (2004, January 23–27). Simulations of optoacoustic wave propagation in light-absorbing media using the finite difference time-domain method. Proceedings of the IEEE Ultrasonics Symposium, 2004, Montreal, QC, Canada.
25. Schwartz, H., Pineau, J.-P., Feuillard, G., Prax, C., and Valiere, J.-C. (2012, January 7–10). Development of flow characterization system using ultrasonic methods. Proceedings of the 2012 IEEE International Ultrasonics Symposium, Dresden, Germany.10.1109/ULTSYM.2012.0363 ↗
26. Fang, L., Yu, X., Zeng, Q., Zhao, N., and Li, X. (2019, January 20–23). Excitation Characteristics of Ultrasonic Probe. Proceedings of the 2019 IEEE International Instru-mentation and Measurement Technology Conference (I2MTC), Auckland, New Zealand.10.1109/I2MTC.2019.8826943 ↗
27. Simulation-Based Inversion for the Characterization of Adhe-sively Bonded Joints Using Ultrasonic Guided Waves10.1109/TUFFC.2022.3175773 ↗
28. Wang, M., and Zheng, D. (2021, January 26–28). Simulation Research on Liquid Film Thickness of Horizontal Gas-liquid Two-phase Flow Based on Ultrasonic Method. Proceedings of the 2021 40th Chinese Control Conference (CCC), Shanghai, China.10.23919/CCC52363.2021.9550014 ↗
29. Ultrasonic inspection of the surface crack for the main shaft of a wind turbine from the end face10.1016/j.ndteint.2020.102283 ↗
30. Simulation Research and Experimental Verification of Non-defect Echoes by Ultra-sonic Testing