本研究的目的是使用非破壞性與聲學的技術監測大面積平板中的結構缺陷。基本原理是使用壓電材料黏貼在基板上，並以電壓驅動壓電片的方式在平板材料產生導波 (蘭姆波)，再根據蘭姆波通過損傷時的波傳特性變化，確定損傷的位置與面積大小。在實驗架構上，使用自行開發的訊號擷取系統，將多通道訊號擷取卡、多工器、損傷定位與定量演算法藉由 C++ 整合至人機介面，且自動化儲存所有的收發路徑資料；接著使用壓電材料搭配軟性電路板自行開發感測器，使其能與目前的系統匹配，增加量測訊號的便利性，最後經由自行開發的演算法將損傷位置及大小以圖形化呈現出來。
本研究使用鋁板以及複合材料板為基板，且因為損傷試片不易大量製作，實驗上使用由美國航太公司 (Acellent Technologies Inc) 提供的損傷貼片來模擬損傷，同時也討論此模擬損傷對於波傳的影響。而自行開發的損傷定位演算法將計算無損傷訊號與實際訊號的差異，稱此差異為DI (Damage Index)。將 DI 數值過大的路徑繪製並找出彼此的交點，即可根據路徑產生的交點範圍，藉由影像處理的方式，正確的尋找損傷的位置與大小。而上述的方法需要有大量路徑的交點才能使準確度提升，因此將感測器採用蜂巢佈局黏貼至待測物上，蜂巢佈局相對於矩形佈局的優點為有較多的收發路徑可以使用，交點數量也會較多，且可以無限延伸感測面積。

The purpose of this study is to utilize non-destructive and ultrasound techniques to detect structural defects in a flat panel. Piezoelectric transducers are attached to the substrate, and voltage is applied to generate guided waves (Lamb waves) in the pate. By analyzing the characteristics of wave propagation when Lamb waves pass through damage, differences between damaged and undamaged signals can be used to identify the locations and sizes of defects. In the experiment, we developed a signal acquisition system that integrates a multi-channel signal acquisition card, multiplexer, damage localization, and quantification algorithms into a user interface using C++. The system automatically stores all the transmit-receive path data. We also developed piezoelectric transducers and integrated them with flexible circuit boards for conveniently measuring transducers’ signals and matching the electronic system. Finally, we present the damage location and size graphically using a self-developed algorithm.

The study uses aluminum plates and composite plates as substrates. Since it is difficult to produce a large number of damaged samples, simulated damages are created using damage patches provided by Acellent Technologies Inc, USA. The influence of these simulated damages on wave propagation is also discussed. The developed damage localization algorithm calculates the difference between undamaged signals and actual signals, referred to as the Damage Index (DI). By plotting the paths with significantly large DI values and identifying their intersection points, the location and size of the damage can be accurately determined using image processing techniques. The accuracy of this method relies on a sufficient number of intersection points; therefore, the sensors are arranged in a honeycomb layout on the test object. The honeycomb layout offers the advantage of providing more transmit-receive paths compared to a rectangular layout, resulting in a greater number of intersection points. Moreover, the sensing area can be extended infinitely.

[1] S. Gholizadeh, “A review of non-destructive testing methods of composite materials,” Procedia Struct. Integr., vol. 1, pp. 50–57, 2016.
[2] H. Lamb, “On waves in an elastic plate,” Proc. R. Soc. Lond. A Math. Phys. Sci., vol. 93, no. 648, pp. 114–128, 1917.
[3] D. Worlton, “Ultrasonic testing with lamb waves,” Office of Scientific and Technical Information (OSTI), 1956
[4] V. Giurgiutiu, J. Bao, and W. Zhao, “Active sensor wave propagation health monitoring of beam and plate structures,” in Smart Structures and Materials 2001: Smart Structures and Integrated Systems, 2001.
[5] S. H. Díaz Valdés and C. Soutis, “Health monitoring of composites using Lamb waves generated by piezoelectric devices,” Plast. Rubber Compos., vol. 29, no. 9, pp. 475–481, 2000.
[6] S. Komarizadehasl, F. Lozano, J. A. Lozano-Galant, G. Ramos, and J. Turmo, “Low-cost wireless Structural Health Monitoring of bridges,” Sensors (Basel), vol. 22, no. 15, p. 5725, 2022.
[7] P. Jiao, K.-J. I. Egbe, Y. Xie, A. Matin Nazar, and A. H. Alavi, “Piezoelectric sensing techniques in structural health monitoring: A state-of-the-art review,” Sensors (Basel), vol. 20, no. 13, p. 3730, 2020.
[8] X. Qing, W. Li, Y. Wang, and H. Sun, “Piezoelectric transducer-based structural health monitoring for aircraft applications,” Sensors (Basel), vol. 19, no. 3, p. 545, 2019.
[9] R. Gorgin, Y. Luo, and Z. Wu, “Environmental and operational conditions effects on Lamb wave based structural health monitoring systems: A review,” Ultrasonics, vol. 105, no. 106114, p. 106114, 2020.
[10] Muller, B. Robertson-Welsh, P. Gaydecki, M. Gresil, and C. Soutis, “Structural health monitoring using lamb wave reflections and total focusing method for image reconstruction,” Appl. Compos. Mater., vol. 24, no. 2, pp. 553–573, 2017.
[11] 邱昱誠，“SMART Layer 系統於結構健康監測之應用研究,”國立成功大學機械工程學系，2019.
[12] 劉基源，“單剪力搭接結構之非破壞結構健康監測與 SMART Layer 系統應用研究,”國立成功大學機械工程學系，2020.
[13] 謝嘉浤，“藍姆波結構健康監測系統於單剪力金屬搭接結構及複合材料補片修補金屬裂紋結構之應用,”國立成功大學機械工程學系， 2021.
[14] C.-D. Chen, C.-H. Hsieh, C.-Y. Liu, Y.-H. Huang, P.-H. Wang, and R.-D. Chien, “Lamb wave–based structural health monitoring for aluminum bolted joints with multiple-site fatigue damage,” J. Aerosp. Eng., vol. 35, no. 6, 2022.
[15] 沈玉杰，“應用藍姆波監測於複合材料疊層板受衝擊後之損傷位置識別,”國立成功大學機械工程學系，2022.
[16] Z. Su and L. Ye, “Lamb wave-based quantitative identification of delamination in CF/EP composite structures using artificial neural algorithm,” Compos. Struct., vol. 66, no. 1–4, pp. 627–637, 2004.
[17] Z. Su and L. Ye, “Digital Damage Fingerprints (DDF) and its application in quantitative damage identification,” Compos. Struct., vol. 67, no. 2, pp. 197–204, 2005.
[18] B. Zhang, X. Hong, and Y. Liu, “Multi-task deep transfer learning method for guided wave-based integrated health monitoring using piezoelectric transducers,” IEEE Sens. J., vol. 20, no. 23, pp. 14391–14400, 2020.
[19] Y. Feng, D. Lu, B. Li, J. Yang, W. Chen, and L. Zhou, “Damage detection in composite structure based on damage recognition an localization algorithm of high accuracy,” in 2014 10th International Conference on Reliability, Maintainability and Safety (ICRMS), 2014, pp. 242–246.
[20] M. S. Hameed and Z. Li, “Transverse damage localization and quantitative size estimation for composite laminates based on lamb waves,” IEEE Access, vol. 7, pp. 174859–174872, 2019.
[21] Z. Wu, K. Liu, Y. Wang, and Y. Zheng, “Validation and evaluation of damage identification using probability-based diagnostic imaging on a stiffened composite panel,” J. Intell. Mater. Syst. Struct., vol. 26, no. 16, pp. 2181–2195, 2015.
[22] S. Shan, J. Qiu, C. Zhang, H. Ji, and L. Cheng, “Multi-damage localization on large complex structures through an extended delay-and-sum based method,” Struct. Health Monit., vol. 15, no. 1, pp. 50–64, 2016.
[23] J. Rajagopalan, K. Balasubramaniam, and C. V. Krishnamurthy, “A single transmitter multi-receiver (STMR) PZT array for guided ultrasonic wave based structural health monitoring of large isotropic plate structures,” Smart Mater. Struct., vol. 15, no. 5, pp. 1190–1196, 2006.
[24] F. Li, H. Peng, and G. Meng, “Quantitative damage image construction in plate structures using a circular PZT array and lamb waves,” Sens. Actuators A Phys., vol. 214, pp. 66–73, 2014.
[25] X. Zeng, B. Zhao, X. Liu, Y. Yu, J. Guo, and X. Qing, “Lamb wave-based damage assessment for CFRP composite structures using a CHMM-based damage localization algorithm and a damage quantitative expression,” Mech. Syst. Signal Process., vol. 184, no. 109750, p. 109750, 2023.
[26] S. Wang, W. Wu, Y. Shen, Y. Liu, and S. Jiang, “Influence of the PZT sensor array configuration on lamb wave tomography imaging with the RAPID algorithm for hole and crack detection,” Sensors (Basel), vol. 20, no. 3, p. 860, 2020.
[27] S. Torkamani, S. Roy, M. E. Barkey, E. Sazonov, S. Burkett, and S. Kotru, “A novel damage index for damage identification using guided waves with application in laminated composites,” Smart Mater. Struct., vol. 23, no. 9, p. 095015, 2014.
[28] X. P. Qing, H.-L. Chan, S. J. Beard, and A. Kumar, “An active diagnostic system for structural health monitoring of rocket engines,” J. Intell. Mater. Syst. Struct., vol. 17, no. 7, pp. 619–628, 2006.
[29] S. Suzuki and K. Be, “Topological structural analysis of digitized binary images by border following,” Comput. Vis. Graph. Image Process., vol. 30, no. 1, pp. 32–46, 1985.
[30] W. Fitzgibbon and R. B. Fisher, “A buyer’s guide to conic fitting,” in Procedings of the British Machine Vision Conference 1995, pp. 513-522, 1995.