A fully non-contact experimental platform for ultrasonic Lamb wave damage detection was constructed,where laser exciting and the scanning laser Doppler vibrometer were used to realize the high-resolution pickup of the...A fully non-contact experimental platform for ultrasonic Lamb wave damage detection was constructed,where laser exciting and the scanning laser Doppler vibrometer were used to realize the high-resolution pickup of the Lamb wave field in the structure,which has overcome the disadvantages of low spatial resolution caused by the conventional contact Lamb wave transducer.In order to suppress the dispersion effect of broadband laser-ultrasonic signal,we proposed time-domain filtering in multi-band method based on wavelet analysis to decompose the broadband signal into multiple narrowband ones and separate the scattering signals effectively without reference signal.On this basis,the total focusing method(TFM)was used for damage imaging.However,when the traditional TFM was applied to image based on ultrasonic Lamb wave,the inherent dispersion characteristic of ultrasonic Lamb wave could lead to the miscalculation of time delay,thus reducing the imaging precision.Therefore,the frequency-domain TFM was developed by applying phase delay in the frequency domain.The logical AND was introduced to synthesize the damage imaging results of multiple narrowband signals to obtain high-precision damage imaging.Our study has shown that the method of time-domain filtering in multi-band combining with frequency-domain TFM can realize non-contact and accurate damage detection in isotropic plate structures,and it is a potential effective method for application in engineering practice.展开更多
Implementing the conventional total focus method(TFM)for visualizing internal damage in reinforced con-crete(RC)is beset with computational challenges and a high dependence on physical principles.To overcome these cha...Implementing the conventional total focus method(TFM)for visualizing internal damage in reinforced con-crete(RC)is beset with computational challenges and a high dependence on physical principles.To overcome these challenges,an efficient total focus imaging method based on deep learning is proposed.This method deals with array ultrasonic time-domain signals from cracked RC beams.A deep neural network(DNN)employing a feature extraction+multilevel feature fusion+matrix construction architec-ture was developed;this architecture enabled the DNN to learn the underlying physical principles of the TFM.The ar-chitecture effectively transformed ultrasonic time-domain signals into a B-scan matrix.Training,validation,and test-ing data were collected by measuring eight RC beams with preset artificial cracks using a low-frequency shear wave ar-ray ultrasonic system.The results demonstrated that the re-constructed B-scan matrices had a peak signal-to-noise ratio of 26.94 dB and a structural similarity index of 0.978.Fur-thermore,the proposed method required 42%fewer floating-point operations compared with physics-based cal-culations,achieving total focus imaging with lower compu-tational cost.The study facilitates the advancement of ultra-sonic total focus imaging of RC structures from physics-based methods to data-driven methods without re-quiring prior physical knowledge,thereby providing robust support for further nondestructive evaluation and quantita-tive analysis.展开更多
The aeroengine casing ring forgings have complex cross-section shapes,when the conventional ultrasonic or phased array is applied to detect such curved surfaces,the inspection images always have low resolution and eve...The aeroengine casing ring forgings have complex cross-section shapes,when the conventional ultrasonic or phased array is applied to detect such curved surfaces,the inspection images always have low resolution and even artifacts due to the distortion of the wave beam.In this article,taking a type of aeroengine casing ring forging as an example,the Total Focusing Method(TFM)algorithms for curved surfaces are investigated.First,the Acoustic Field Threshold Segmentation(AFTS)algorithm is proposed to reduce background noise and data calculation.Furthermore,the Vector Coherence Factor(VCF)is adopted to improve the lateral resolution of the TFM imaging.Finally,a series of 0.8 mm diameter Side-Drilled Holes(SDHs)are machined below convex and concave surfaces of the specimen.The quantitative comparison of the detection images using the conventional TFM,AFTS-TFM,VCF-TFM,and AFTS-VCF-TFM is implemented in terms of data volume,imaging Signal-to-Noise Ratio(SNR),and defect echo width.The results show that compared with conventional TFM,the data volume of AFTS-VCF-TFM algorithm for convex and concave is decreased by 32.39%and 73.40%,respectively.Moreover,the average SNR of the AFTS-VCF-TFM is gained up to 40.0 dB,while the average 6 dB-drop echo width of defects is reduced to 0.74 mm.展开更多
基金This work is supported by the National Science Foundation of China(NSFC)with agreement No.11520101001.This paper continues to study on the basis of the work of Chen Li,Fan Min and Zhou Lei.thank you!I also would like to thank my mentor Professor Luo Ying for his guidance and help.
文摘A fully non-contact experimental platform for ultrasonic Lamb wave damage detection was constructed,where laser exciting and the scanning laser Doppler vibrometer were used to realize the high-resolution pickup of the Lamb wave field in the structure,which has overcome the disadvantages of low spatial resolution caused by the conventional contact Lamb wave transducer.In order to suppress the dispersion effect of broadband laser-ultrasonic signal,we proposed time-domain filtering in multi-band method based on wavelet analysis to decompose the broadband signal into multiple narrowband ones and separate the scattering signals effectively without reference signal.On this basis,the total focusing method(TFM)was used for damage imaging.However,when the traditional TFM was applied to image based on ultrasonic Lamb wave,the inherent dispersion characteristic of ultrasonic Lamb wave could lead to the miscalculation of time delay,thus reducing the imaging precision.Therefore,the frequency-domain TFM was developed by applying phase delay in the frequency domain.The logical AND was introduced to synthesize the damage imaging results of multiple narrowband signals to obtain high-precision damage imaging.Our study has shown that the method of time-domain filtering in multi-band combining with frequency-domain TFM can realize non-contact and accurate damage detection in isotropic plate structures,and it is a potential effective method for application in engineering practice.
基金Science & Technology Specific Project of Jiangsu Province (No. BZ2024047)Key R&D Program of Ningbo (No. 2024H013)the National Natural Science Foundation of China (No. W2412092)。
文摘Implementing the conventional total focus method(TFM)for visualizing internal damage in reinforced con-crete(RC)is beset with computational challenges and a high dependence on physical principles.To overcome these challenges,an efficient total focus imaging method based on deep learning is proposed.This method deals with array ultrasonic time-domain signals from cracked RC beams.A deep neural network(DNN)employing a feature extraction+multilevel feature fusion+matrix construction architec-ture was developed;this architecture enabled the DNN to learn the underlying physical principles of the TFM.The ar-chitecture effectively transformed ultrasonic time-domain signals into a B-scan matrix.Training,validation,and test-ing data were collected by measuring eight RC beams with preset artificial cracks using a low-frequency shear wave ar-ray ultrasonic system.The results demonstrated that the re-constructed B-scan matrices had a peak signal-to-noise ratio of 26.94 dB and a structural similarity index of 0.978.Fur-thermore,the proposed method required 42%fewer floating-point operations compared with physics-based cal-culations,achieving total focus imaging with lower compu-tational cost.The study facilitates the advancement of ultra-sonic total focus imaging of RC structures from physics-based methods to data-driven methods without re-quiring prior physical knowledge,thereby providing robust support for further nondestructive evaluation and quantita-tive analysis.
基金supported by the National Key Research and Development Program of China(No.2019YFB1704500)the National Natural Science Foundation of China(No.51875428)+3 种基金the Key Research and Development Program of Hubei Province,China(No.2020BAB144)the Excellent Youth Foundation of Hubei Province,China(No.2019CFA041)the Innovative Research Team Development Program of Ministry of Education of China(No.IRT_17R83)the 111 Project of China(No.B17034)。
文摘The aeroengine casing ring forgings have complex cross-section shapes,when the conventional ultrasonic or phased array is applied to detect such curved surfaces,the inspection images always have low resolution and even artifacts due to the distortion of the wave beam.In this article,taking a type of aeroengine casing ring forging as an example,the Total Focusing Method(TFM)algorithms for curved surfaces are investigated.First,the Acoustic Field Threshold Segmentation(AFTS)algorithm is proposed to reduce background noise and data calculation.Furthermore,the Vector Coherence Factor(VCF)is adopted to improve the lateral resolution of the TFM imaging.Finally,a series of 0.8 mm diameter Side-Drilled Holes(SDHs)are machined below convex and concave surfaces of the specimen.The quantitative comparison of the detection images using the conventional TFM,AFTS-TFM,VCF-TFM,and AFTS-VCF-TFM is implemented in terms of data volume,imaging Signal-to-Noise Ratio(SNR),and defect echo width.The results show that compared with conventional TFM,the data volume of AFTS-VCF-TFM algorithm for convex and concave is decreased by 32.39%and 73.40%,respectively.Moreover,the average SNR of the AFTS-VCF-TFM is gained up to 40.0 dB,while the average 6 dB-drop echo width of defects is reduced to 0.74 mm.
