不锈钢薄板激光焊搭接接头超声波检测研究
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摘要
本文对不锈钢薄板激光焊搭接接头的超声波检测方法及理论进行了系统研究。结果表明，对应焊接接头不同部位扫描点的A扫描信号存在显著差异。在母材区域，上层钢板下表面处的反射回波幅值最高；当探头逐渐接近焊缝中心时，由于反射面积的减少，反射回波幅值随之降低；当探头位于焊缝中心时，反射回波幅值达到最低值。同时，A扫描信号的频谱分析结果表明，当探头由母材区域移动至焊缝中心时，信号的主频率由2MHz变为1.25MHz，即反射源由上层钢板下表面变为下层钢板下表面。A扫描信号中两次底面回波之间的散射波分析结果表明，焊缝与母材之间的熔合线界面处不存在明显的反射特征，这也说明在使用15MHz的纵波探头对不锈钢搭接接头进行检测时，不能够直接获得焊缝的熔深信息。原始A扫描信号的小波分析结果表明，信号的主要成分为上层钢板下表面的反射回波，能量主要集中在信号的低频部分；随着频率的升高，反射回波特征逐渐减弱。使用小波包技术对A扫描信号的高频部分进行进一步的分解，确定上层钢板下表面的反射回波集中在0~6.25MHz频带内；当探头由母材向焊缝中心移动时，反射回波幅值随反射面积的减少而降低，这种变化在0~37.5MHz频带内均有不同程度的表现。同时发现，晶粒散射波主要集中37.5~43.75MHz频带内，当探头由母材向焊缝中心移动时，晶粒逐渐粗大，散射波有变强的趋势。超声波检测过程的有限元仿真分析结果表明，上层钢板下表面的反射回波幅值与声束的反射面积呈正比；当探头位于熔合边缘时，回波强度衰减度的仿真计算结果为-6.77dB，与实测信号基本一致。为弥补传统的6dB法的不足，本文提出以等效宽度为目的的计算模型，计算结果表明，检测区域接头内部两层钢板接触面处的熔宽计算精度可以达到0.05mm，完全满足工业条件下焊接质量在线检测的需求。
Along with the rapid development of automobile and rail transit manufacturing, themanufacture technology of bodywork based on laser welding has been researched anddeveloped in the main railway car manufacture enterprise at home and abroad to increase thecompetiveness of high quality vehicles. Lap joint is mostly used in the structure of stainlesssteel bodywork. Partial penetrated laser welding is mainly used to acquire the joint. Duringlaser welding, defects such as incomplete fusion and insufficient penetration easily appeardue to the installation ways and the abnormal fluctuation of welding parameters. Therefore,laser welding process control and quality evaluation is a key problem that limits theapplication of laser welding technology.
Two kinds of method are mostly used for laser welding evaluation. They are onlineevaluation based on the process control and postweld destructive inspection. The former canprovide quality information partly and can not represent the quality information of jointsaccurately. The latter makes destruction of the joint. Its cost is high and the testing period islong. It can be applied in experimental research and is not suitable for welding production.Thus, efficient and reliable nondestructive testing methods by ultrasonic should bedeveloped to analyze the weld quality of laser welded lap joints of thin stainless steel sheet.Further study on imaging principle, signal de-noising and weld width calculation method hashigh academic and practical value.
In this paper, an ultrasonic testing system for laser welding was developed. A-scansignal variation at different positions in the joint was studied using the longitudinal wavereflection method. Experimental results show that there exists relationship between thereflection echo amplitude from the lower surface of the upper steel sheet and the fusion statein the joint. When the probe is at the base metal region, the highest amplitude is received.When the probe moves to the joint centerline, the amplitude decreases due to the decrease ofthe reflection area. When the probe is at the joint centerline, the amplitude is the smallest.Meanwhile, the spectrum analysis of A-scan signal indicates that when the probe movesfrom the base metal region to the joint centerline, the main frequency ratio of the signalchanges from2MHz into1.25MHz. It means that the reflection source changes from thelower surface of the upper steel sheet into the lower surface of the lower steel sheet.
When scanning across the joint, there exists relationship between the reflection echo amplitude from the lower surface of the upper steel sheet and the weld width in the joint.C-scan image constructed according to the reflection echo can represent the fusion state inthe joint. However, the analysis on scattered wave of the reflection echo show that there isno reflection characteristic at the fusion line between the base metal and the weld metal. Itmeans that the information of weld penetration can not be received when15MHz longitudeprobe is used to test the laser welded lap joints of stainless steel sheet. Welding parametersand materials characteristic should be in considered to estimate the weld width.
In order to further understand the detail characteristic of A-scan signal, wavelettransform technology is used to decompose the original signal. The spectrum composition ofA-scan signal and the jump feature in time domain are analyzed. Three level waveletdecomposition of db4result shows that the signal mainly comprises reflection echo from thelower surface of the upper sheet. The energy mainly exists in the low frequency part. Withthe increase of the frequency, the reflection echo decreases. Through further decomposingthe A-scan signal at high frequency part by means of wavelet transform, it can be conformedthat the reflection echo mainly exists in the frequency range of0~6.25MH. When the probemoves to the joint centerline, the amplitude decreases due to the decrease of the reflectionarea. Such change appears at the frequency range of0~37.5MHz. it is found that thegrain-scattered wave mainly exists in the frequency range of37.5~43.75MHz. When theprobe moves from the base metal to the weld, the grain coarsening causes increase of thescatter wave. Wavelet soft-threshold denoising method is employed to eliminate noise of theA-scan signal based on wavelet decomposition. It can eliminate the effect of scattering noisein the high frequency range so as to strengthen the characteristic of reflection echo. Thus, thequality of C-scan image can be increased.
Finite element simulation model of ultrasonic testing on laser welding joints of stainlesssteel sheet is constructed to further analyze the propagation characteristics of ultrasonic inthe joint and the scattering rules in the joint. Experimental result show that when the probemoves to the joint centerline, the amplitude decreases due to the decrease of the reflectionarea. When the probe is at the fusion line, the simulation value of reflection echo attenuationis-6.77dB, which is consistent with the measured value. When the weld width is smallerthan the ultrasonic sound wavelength, the intensity of the reflection echo does not changeobviously. Focusing ultrasonic probe with high frequency and fine ultrasonic beam should bechosen to acquire relatively small lateral resolution. Moreover, testing process with a tiltprobe is simulated. The result shows that the incident angle of the probe has effect on theamplitude of reflection echo from the lower surface of the steel sheet. With the increase ofthe incident angle, the amplitude decreases. There exists linear relationship between the relative attenuation degree and the incident angle.
In order to obtain quantitative testing of the weld width, C-scan image of the joint isprocessed and the joint centerline is automatically extracted to construct the model forcalculating the weld width. The analyzing result shows that there exists step edge in C-scanimage constructed according to the reflection echo from the lower surface of the upper sheet.Edge testing of the original image is applied by Roberts operator. Thus the image isdecomposed subsets with obvious weld characteristics. Equivalent centerline of the weld canbe extracted from the decomposed C-scan image by Hough transformation technique. Theend and the discontinuity of the weld can be recognized considering that the weld is round.Thus the endpoint location of the weld can be determined.
In the whole scanning region, there exists corresponding relationship between theamplitude of the reflection echo from the lower surface of the upper sheet and the scanningpoint. Therefore, amplitude-position curve is established statistical principle. It can representthe information of the equivalent weld width. The curve is simplified and fitted by leastsquare method. Finally, calculation formula of equivalent weld width is constructed.Calculating result shows that rapid convenient calculation of the weld width can be acquired.It can meet the demand of online testing in the industrial production with high calculationaccuracy and reliability.
Further understanding of the ultrasonic testing method and theory for laser welded lapjoints of stainless steel sheet is obtained. It can provide detailed experimental data andtheoretical basis for ultrasonic testing.
Along with the rapid development of automobile and rail transit manufacturing, themanufacture technology of bodywork based on laser welding has been researched anddeveloped in the main railway car manufacture enterprise at home and abroad to increase thecompetiveness of high quality vehicles. Lap joint is mostly used in the structure of stainlesssteel bodywork. Partial penetrated laser welding is mainly used to acquire the joint. Duringlaser welding, defects such as incomplete fusion and insufficient penetration easily appeardue to the installation ways and the abnormal fluctuation of welding parameters. Therefore,laser welding process control and quality evaluation is a key problem that limits theapplication of laser welding technology.
Two kinds of method are mostly used for laser welding evaluation. They are onlineevaluation based on the process control and postweld destructive inspection. The former canprovide quality information partly and can not represent the quality information of jointsaccurately. The latter makes destruction of the joint. Its cost is high and the testing period islong. It can be applied in experimental research and is not suitable for welding production.Thus, efficient and reliable nondestructive testing methods by ultrasonic should bedeveloped to analyze the weld quality of laser welded lap joints of thin stainless steel sheet.Further study on imaging principle, signal de-noising and weld width calculation method hashigh academic and practical value.
In this paper, an ultrasonic testing system for laser welding was developed. A-scansignal variation at different positions in the joint was studied using the longitudinal wavereflection method. Experimental results show that there exists relationship between thereflection echo amplitude from the lower surface of the upper steel sheet and the fusion statein the joint. When the probe is at the base metal region, the highest amplitude is received.When the probe moves to the joint centerline, the amplitude decreases due to the decrease ofthe reflection area. When the probe is at the joint centerline, the amplitude is the smallest.Meanwhile, the spectrum analysis of A-scan signal indicates that when the probe movesfrom the base metal region to the joint centerline, the main frequency ratio of the signalchanges from2MHz into1.25MHz. It means that the reflection source changes from thelower surface of the upper steel sheet into the lower surface of the lower steel sheet.
When scanning across the joint, there exists relationship between the reflection echo amplitude from the lower surface of the upper steel sheet and the weld width in the joint.C-scan image constructed according to the reflection echo can represent the fusion state inthe joint. However, the analysis on scattered wave of the reflection echo show that there isno reflection characteristic at the fusion line between the base metal and the weld metal. Itmeans that the information of weld penetration can not be received when15MHz longitudeprobe is used to test the laser welded lap joints of stainless steel sheet. Welding parametersand materials characteristic should be in considered to estimate the weld width.
In order to further understand the detail characteristic of A-scan signal, wavelettransform technology is used to decompose the original signal. The spectrum composition ofA-scan signal and the jump feature in time domain are analyzed. Three level waveletdecomposition of db4result shows that the signal mainly comprises reflection echo from thelower surface of the upper sheet. The energy mainly exists in the low frequency part. Withthe increase of the frequency, the reflection echo decreases. Through further decomposingthe A-scan signal at high frequency part by means of wavelet transform, it can be conformedthat the reflection echo mainly exists in the frequency range of0~6.25MH. When the probemoves to the joint centerline, the amplitude decreases due to the decrease of the reflectionarea. Such change appears at the frequency range of0~37.5MHz. it is found that thegrain-scattered wave mainly exists in the frequency range of37.5~43.75MHz. When theprobe moves from the base metal to the weld, the grain coarsening causes increase of thescatter wave. Wavelet soft-threshold denoising method is employed to eliminate noise of theA-scan signal based on wavelet decomposition. It can eliminate the effect of scattering noisein the high frequency range so as to strengthen the characteristic of reflection echo. Thus, thequality of C-scan image can be increased.
Finite element simulation model of ultrasonic testing on laser welding joints of stainlesssteel sheet is constructed to further analyze the propagation characteristics of ultrasonic inthe joint and the scattering rules in the joint. Experimental result show that when the probemoves to the joint centerline, the amplitude decreases due to the decrease of the reflectionarea. When the probe is at the fusion line, the simulation value of reflection echo attenuationis-6.77dB, which is consistent with the measured value. When the weld width is smallerthan the ultrasonic sound wavelength, the intensity of the reflection echo does not changeobviously. Focusing ultrasonic probe with high frequency and fine ultrasonic beam should bechosen to acquire relatively small lateral resolution. Moreover, testing process with a tiltprobe is simulated. The result shows that the incident angle of the probe has effect on theamplitude of reflection echo from the lower surface of the steel sheet. With the increase ofthe incident angle, the amplitude decreases. There exists linear relationship between the relative attenuation degree and the incident angle.
In order to obtain quantitative testing of the weld width, C-scan image of the joint isprocessed and the joint centerline is automatically extracted to construct the model forcalculating the weld width. The analyzing result shows that there exists step edge in C-scanimage constructed according to the reflection echo from the lower surface of the upper sheet.Edge testing of the original image is applied by Roberts operator. Thus the image isdecomposed subsets with obvious weld characteristics. Equivalent centerline of the weld canbe extracted from the decomposed C-scan image by Hough transformation technique. Theend and the discontinuity of the weld can be recognized considering that the weld is round.Thus the endpoint location of the weld can be determined.
In the whole scanning region, there exists corresponding relationship between theamplitude of the reflection echo from the lower surface of the upper sheet and the scanningpoint. Therefore, amplitude-position curve is established statistical principle. It can representthe information of the equivalent weld width. The curve is simplified and fitted by leastsquare method. Finally, calculation formula of equivalent weld width is constructed.Calculating result shows that rapid convenient calculation of the weld width can be acquired.It can meet the demand of online testing in the industrial production with high calculationaccuracy and reliability.
Further understanding of the ultrasonic testing method and theory for laser welded lapjoints of stainless steel sheet is obtained. It can provide detailed experimental data andtheoretical basis for ultrasonic testing.
引文
[1]窦广旭,王卫朝.不锈钢车体制造技术[J].机车车辆工艺,2005,10(5):12-16.
[2]米谷弘.车体制造中的新型激光焊接技术[J].国外机车车辆工艺,2008,7(4):1-4.
[3]彭惠民.铁道车辆的激光焊接[J].铁道机车车辆工人,2008,8(8):32.
[4] EMMELMANN C. Introduction to indusial laser material processing [M]. Rofinsinar,2000.
[5] McNab A, Thomson J. An eddy current array instrument for application on ferriticwelds [J]. NDT&E International,1995,28(2):103-112.
[6] Rao B P C, Raj B, Jayakumar T, et al. An artificial neural network for eddy currenttesting of austenitic stainless steel welds [J]. NDT&E International.2002,35(2):393-398.
[7] Noritaka Y, Eiji M, Ladislav J, et al. Application of eddy current inversion technique tothe sizing of defects in Inconel welds with rough surfaces [J]. Nuclear Engineering andDesign.2005,235:1469-1480.
[8]姚立东,赖圣,付跃文,等.飞机多层金属结构脉冲涡流检测提离效应的抑制[J].失效分析与预防,2013,8(3):131-135.
[9] Babot D, Berodias G, Peix G. Detection and sizing by X-ray compton scattering ofnear-surface cracks under weld deposited cladding [J]. NDT&E International,1991,24(5):247-251.
[10]孙怡,孙洪雨,白鹏,等.射线焊缝图像中缺陷的实时检测方法[J].焊接学报,2004,22(2):115-118.
[11]陈明,马跃洲,陈光. X射线线阵实时成像焊缝缺陷检测方法[J].焊接学报,2007,28(6):81-84.
[12]丁克勤,陈光,张旭.基于线阵探测器的Ｘ和γ射线角焊缝检测试验分析[J].无损检测,2012,34(9):27-29.
[13] Ditchburn R J, Burke S K, Scala C M. NDT of welds: state of the art [J]. NDT&EInternational,1996,29(2):11l-l17.
[14] Hübschen G, Salzburger H J. Inspection of dissimilar metal welds using horizontallypolarized shear (SH-)waves and electromagnetic ultrasonic (EMUS-) probes [J].International Journal of Pressure Vessels and Piping,1989,39(4):331-344.
[15] Nichols R W, Crutzen S. Ultrasonic inspection of heavy section steel components: thePISC II final report [C]. London:1988.
[16] Crutzen S, Jehenson P, Nichols R, et al.‘PISC III: Status report and future trends’ in10th International Conference on NDE in the Nuclear and Pressure Vessel Industries
[C]. ASM International: Materials Park,1990.
[17] Chen Z H, Shi Y W, Jiao B Q, et al. Ultrasonic nondestructive evaluation of spot weldsfor zinc-coated high strength steel sheet based on wavelet packet analysis [J]. Journalof Materials Processing Technology,2009,209(5):2329-2337.
[18] Denisov A A, Shakarji C M, Lawford B B, et al. Spot weld analysis with2D ultrasonicarrays [J]. Journal of Research of the National Institute of Standards and Technology,2004,109:233-244.
[19] Maev R G, Ptchelintsev A, Denisov A A. Ultrasonic imaging with2D matrixtransducers [J]. Journal for Material Characterization,2000,3:326-332.
[20] Mozurkewich G, Ghaffari B, Potter T J. Spatially resolved ultrasonic attenuation inresistance spot welds [J]. Implications for Nondestructive Testing,2008,48:343-350.
[21] Siemiatko W, Chojecki, Rafal, et al. Mobile system for non destructive testing of weldjoints via time of flight diffraction (TOFD) technique [J]. Robot Motion and Control,2012,422:65-74.
[22] Ataby A, Nuaimy W, Brett C R. Automatic detection and classification of weld flawsin TOFD data using wavelet transform and support vector machines [J]. Insight,2010,52(11):597-602.
[23] Wang W B, Wang Y Q, Qing Z. Signal analysis of ultrasonic system with phased arrayand TOFD technique applied on inspecting pipeline welds defects [J]. EngineeringStructural Integrity: Research, Development and Application,2007, S1:1235-1238.
[24] Praveena A, Vijayarekhaa K, Saju T, et al. Signal quality enhancement using higherorder wavelets for ultrasonic TOFD signals from austenitic stainless steel welds [J].Ultrasonics,2013,53(7):1288-1292.
[25] Brekow G, Erhard A. Weld examination on tube test specimen using TOFD [J].Material Prufung,2000,42(11-12):437-443.
[26] Gang T, Xu Y, Chi D Z, et al. Ultrasonic TOFD testing for aluminum alloy weld ofthick plate [J]. Transactions of Nonferrous Metals Society of China,2005,15:79-82.
[27] Vicen R, Gil R, Jarabo P, et al. N-linear filtering of ultrasonic signals using neuralnetworks [J]. Ultrasonics,2004,42(1-9):355-360.
[28] Liu Z Q, Lu M D, Wei M. Structure noise reduction of ultrasonic signals usingartificial neural network adaptive filtering [J]. Ultrasonics,1997,35(4):325-328.
[29] Andria G, Attivissimo F, Giaquinto N. Digital signal processing techniques for accurateultrasonic sensor measurement [J]. Measurement,2001,30(2):105-114.
[30] Vijayalakshmi K, Muthupandi V, Jayachitra R. Influence of heat treatment on themicrostructure, ultrasonic attenuation and hardness of SAF2205duplex stainless steel[J]. Materials Science and Engineering: A,2011,529(25):447-451.
[31] Izquierdo M A G, Hernández M G, Anaya J J. Time-varying prediction filter forstructural noise reduction in ultrasonic NDE [J]. Ultrasonics,2006,44(22):1001-1005.
[32] Ka ys R, Svilainis L. Ultrasonic detection and characterization of delaminations inthin composite plates using signal processing techniques [J]. Ultrasonics,1997,35(5):367-383.
[33] Zhang G M, Zhang C Z. Sparse signal representation and its applications in ultrasonicNDE [J]. Ultrasonics,2012,52(3):351-363.
[34] Shakibi B, Honarvar F, Moles M D C, et al. Resolution enhancement of ultrasonicdefect signals for crack sizing [J]. NDT&E International,2012,52(11):37-50.
[35] Izquierdo M A G, Anaya J J, Mart n ez O. Multi-pattern adaptive inverse filter forreal-time deconvolution of ultrasonic signals in scattering media [J]. Sensors andActuators A: Physical,1999,76(1-3):26-31.
[36] Wei L, Huang Z Y, Que P W. Sparse deconvolution method for improving thetime-resolution of ultrasonic NDE signals [J]. NDT&E International,2009,42(5):430-434.
[37] Drai R, Khelil M, Benchaala A. Time frequency and wavelet transform applied toselected problems in ultrasonics NDE [J]. NDT&E International,2002,35(8):567-572.
[38] Rodr g uez M A, Lázaro J C, Ramos A. Ultrasonic flaw detection in NDE of highlyscattering materials using wavelet and Wigner-Ville transform processing [J].Ultrasonics,2004,42(1-9):847-851.
[39] Yang W X, Hull J B, Seymour M D. A contribution to the applicability of complexwavelet analysis of ultrasonic signals [J]. NDT&E International,2004,37(6):497-504.
[40] Song S P, Que P W. Wavelet based noise suppression technique and its application toultrasonic flaw detection [J]. Ultrasonics,2006,44:188-193.
[41] Praveen A, Vijayarekha K, Abraham S T. Signal quality enhancement using higherorder wavelets for ultrasonic TOFD signals from austenitic stainless steel welds [J].Ultrasonics,2013,53:1288-1292.
[42] Bettayeb F, Haciane S, Aoudia S. Improving the time resolution and signal noise ratioof ultrasonic testing of welds by the wavelet packet [J]. NDT&E International,2005,38,(6):478-484.
[43] Tadao O, Tadashi N, Akinori Y, Osamu S, Fumi F, Shuichi Y, Hiroshi H. Humanultrasonic hearing is induced by a direct ultrasonic stimulation of the cochlea [J].Neuroscience Letters,2013,539(28):71-76.
[44] Javadi Y, Akhlaghi M, Najafabadi M A. Using finite element and ultrasonic method toevaluate welding longitudinal residual stress through the thickness in austeniticstainless steel plates [J]. Materials&Design,2013,45(3):628-642.
[45] Ke W, Castaings M, Bacon C.3D finite element simulations of an air-coupledultrasonic NDT system [J]. NDT&E International,2009,42(6):524-533.
[46] Sadeghi S, Najafabadi M A, Javadi Y. Using ultrasonic waves and finite elementmethod to evaluate through-thickness residual stresses distribution in the friction stirwelding of aluminum plates [J]. Materials&Design,2013,52(12):870-880.
[47] Hosokawa A. Ultrasonic pulse waves in cancellous bone analyzed by finite-differencetime-domain methods [J]. Ultrasonics,2006,44(12):227-231.
[48] Vanhille C, Conde C, Campos-Pozuelo C. Finite-difference and finite-volume methodsfor nonlinear standing ultrasonic waves in fluid media [J]. Ultrasonics,2004,42(1-9):315-318.
[49] Nguyen V H, Naili S, Sansalone V. Simulation of ultrasonic wave propagation inanisotropic cancellous bone immersed in fluid [J]. Wave Motion,2010,47(2):117-129.
[50] Doyle P A, Scala C M. Near-field ultrasonic Rayleigh waves from a laser line source[J]. Ultrasonics,1996,34(1):1-8.
[51] Tan A, Hirose S, Wang C Y. A2D time-domain BEM for transient wave scatteringanalysis by a crack in anisotropic solids [J]. Engineering Analysis with BoundaryElements,2005,29(6):610-623.
[52] Li H L, Chong C P, Chan H L W. Finite element analysis on1-3piezocomposite ringsfor ultrasonic transducer applications [J]. Ceramics International,2004,30(7):1827-1830.
[53] Zhang J Y, Xu W J, Carlier J. Modelling and simulation of high-frequency (100MHz) ultrasonic linear arrays based on single crystal LiNbO3[J]. Ultrasonics,2012,52(1):47-53.
[54]施义茂,张建寰,陈仲怀.超声相控阵探头声场优化设计仿真.厦门大学学报(自然科学版),2013,52(3):370-375.
[55]肖砚,王强,胡栋.复杂工件内部缺陷超声柔性探头检测数值仿真[J].湖南科技大学学报(自然科学版),2013,27(4):65-68.
[56] Baek E, Yim H. Numerical modeling and simulation for ultrasonic inspection ofanisotropic austenitic welds using the mass-spring lattice model [J]. NDT&EInternational,2011,44(7):571-582.
[57] Castagnede B, Aknine A, Melon M. Ultrasonic characterization of the anisotropicbehavior of air-saturated porous materials [J]. Ultrasonics1998,36(1-5):323-341.
[58] Nico F, Degrieck J, Leroy O. Ultrasonic polar scans: Numerical simulation ongenerally anisotropic media [J]. Ultrasonics,2006,45(1-4):32-39.
[59] Schubert M, Grill W. Determination of the group and phase velocity surfaces ofacoustic waves in anisotropic materials by ultrasonic acoustic microscopy with phasecontrast [J]. Ultrasonics,1998,36(1-5):367-370.
[60] Kolkoori S, Hoehne C, Prager J. Quantitative evaluation of ultrasonic C-scan image inacoustically homogeneous and layered anisotropic materials using three dimensionalray tracing method [J]. Ultrasonics,2013,19:1-11.
[61] Kolkoori S R,. Rahman M U, Chinta P K. Ultrasonic field profile evaluation inacoustically inhomogeneous anisotropic materials using2D ray tracing model:Numerical and experimental comparison [J]. Ultrasonics,2013,53(2):396-411.
[62] Chassignole B, Guerjouma R E, Ploix M A. Ultrasonic and structural characterizationof anisotropic austenitic stainless steel welds: Towards a higher reliability in ultrasonicnon-destructive testing [J]. NDT&E International,2010,43(4):273-282.
[63] Apfe A, Moysan J, Corneloup G. Coupling an ultrasonic propagation code with amodel of the heterogeneity of multipass welds to simulate ultrasonic testing [J].Ultrasonics,2005,43:447-456.
[64] Eriksson A S, Mattsson J, Niklasson A J. Modelling of ultrasonic crack detection inanisotropic materials [J]. NDT&E International,2000,33(7):441-451.
[65] Ye J. Development of an ultrasonic ray model for phased array ultrasonic testing inanstenitic weldments [C].17thWorld Conference on Nondestructive Testing,2008.
[66] Liu Y, G X S, Zhang D, Gong X F. A simulation study of ultrasonic imaging ofmicro-crack in bone based on time-reversed approach [J]. Acta Acustica,2011,36(2):179-184.
[67] Zhou Y Q, Wang Z H, Ma H W. Numerical simulation of crack detection in pipes usingultrasonic longitudinal guided-wave [J]. Journal of North University of China (NaturalScience Edition),2006,27(3):258-262.
[68] Li Z Q, Lei J, Pan W F. Numerical simulation of cracks detection using ultrasonicwave [J]. Journal of Wuhan University of Technology,2006,28(10):52-54.
[69] He C F, Li W,Wu B. Numerical simulation of longitudinal crack detection in pipesusing ultrasonic guided wave T(0,1) mode [J]. Journal of Beijing University ofTechnology,2007,33(10):1009-1013.
[70] Mitsui H, Ebihara T, Mizutani K. Simulation of internal crack detection inshape-distorted cast billets using time-of-flight measurements of longitudinalultrasonic waves [J]. Materials Transactions,2012,53(2):337-341.
[71] Wu S J, Kuo I, Shung K K. Boundary element simulation of backscattering propertiesfor red blood with high frequency ultrasonic transducers [J]. Ultrasonics,2005,43(3):145-151.
[72] Wang L G, Deng J J, Shen J Z. A time-domain boundary element model for ultrasonicpulse echo [J]. Ultrasonics,1997,35(2):125-130.
[73]王路根,邓京军,沈建中.一种计算固体中超声脉冲散射声场的边界元法[J].声学学报,1997,22(1):28-32.
[74] Ohyama S, Takayama J, Watanabe Y. Temperature distribution and wind vectormeasurement using ultrasonic CT based on the time of flight detection [J]. Sensors andActuators A: Physical,2009,151(2):159-167.
[75] Shiels R, Leibel S, Order S, et al. The value of ultrasonic imaging and CT scanning inplanning the radiotherapy for prostatic carcinoma [J]. International Journal ofRadiation Oncology Biology Physics,1978,4(2):201.
[76] Zhu N, Jiang Y, Kato S. Ultrasonic computerized tomography (CT) for temperaturemeasurements with limited projection data based on extrapolated filtered backprojection (FBP) method [J]. Energy,2005,30(2-4):509-522.
[77] Sciubba D M, Burdette C, Pennant W A, et al. Percutaneous CT-Guided conformalultrasonic ablation of vertebral tumors using a rabbit tumor model [J]. Journal ofVascular and Interventional Radiology,2008,19(2):136-141.
[78] Weill F, Brun P, Rohmer P, et al. Migrations of fluid of pancreatic origin: ultrasonicand CT study of28cases [J]. Ultrasound in Medicine&Biology,1983,9(5):485-496.
[79] Saigusa H, Saigusa M, Aino I. M-Mode color doppler ultrasonic imaging of verticaltongue movement during articulatory movement [J]. Journal of Voice,2006,20(1):38-45.
[80] McHugh R, McDicken W N, Bow C R, et al. An ultrasonic pulsed doppler instrumentfor monitoring human fetal breathing in utero [J]. Ultrasound in Medicine&Biology,1978,3(4):381-384.
[81] Awrangjeb M, Lu G J. Techniques for efficient and effective transformed imageidentification [J]. Journal of Visual Communication and Image Representation,2009,20(8):511-520.
[82] Theodoropoulos G, Loumos V, Anagnostopoulos C. A digital image analysis and neuralnetwork based system for identification of third-stage parasitic strongyle larvae fromdomestic animals [J]. Computer Methods and Programs in Biomedicine,2000,62(2):69-76.
[83] Cécile B, Fran oise K S, Fran ois V D. Image processing to facilitate imageidentification by partially-sighted [J]. International Congress Series,2005,1282(9):980-984.
[84] Katayama T, Hirai T. Parameter identification for noisy image via the EM algorithm [J].Signal Processing,1990,20(1):15-24.
[85] Rahman Z. Jobson D J, Woodell G A. Investigating the relationship between imageenhancement and image compression in the context of the multi-scale retinex [J].Journal of Visual Communication and Image Representation,2011,22(3):237-250.
[86] Huang K Q, Wang Q, Wu Z Y. Natural color image enhancement and evaluationalgorithm based on human visual system [J]. Computer Vision and ImageUnderstanding,2006,103(1):52-63.
[87] Bismuth V, Vaillant R, Funck F, et al. A comprehensive study of stent visualizationenhancement in X-ray images by image processing means [J]. Medical Image Analysis,2011,15(4):565-576.
[88] Chen B, Latifi S, Kanai J. Edge enhancement of remote sensing image data in the DCTdomain [J]. Image and Vision Computing,1999,17(12):913-921.
[89] Chung K L, Yang W J, Yan W M. Efficient edge-preserving algorithm for colorcontrast enhancement with application to color image segmentation [J]. Journal ofVisual Communication and Image Representation,2008,19(5):299-310.
[90] Sikora T. Low Complexity shape-adaptive DCT for coding of arbitrarily shaped imagesegments [J]. Signal Processing: Image Communication,1995,7(4-6):381-395.
[91] Yi J W, Cho S J, Kim W J, et al. A new coding algorithm for arbitrarily shaped imagesegments [J].Signal Processing: Image Communication,1998,12(3):231-242.
[92] Koike K, Nagano S, Ohmi M. Lineament analysis of satellite images using a SegmentTracing Algorithm (STA)[J]. Computers&Geosciences,1995,21(9):1091-1104.
[93] Bigand A, Bouwmans T, Dubus J P. Extraction of line segments from fuzzy images [J].Pattern Recognition Letters,2001,22(13):1405-1418.
[94] Kim D S, Lee K H. Segment-based region of interest generation for pedestriandetection in far-infrared images [J]. Infrared Physics&Technology,2013,61:120-128.
[95] Pavlidis G, Tsompanopoulos A, Papamarkos N. A multi-segment image coding andtransmission scheme [J]. Signal Processing,2005,85(9):1827-1844.
[96] Thrift P R., Dunn S M. Approximating point-set images by line segments using avariation of the Hough transform [J]. Computer Vision, Graphics, and ImageProcessing,1983,21(3):383-394.
[97]刘海华,陈心浩,高智勇.基于形态学操作和模糊聚类技术的超声图像分割[J].电子学报,2007,35(7):1307-1312.
[98]盛朝阳,刚铁,迟大钊.基于分水岭方法的超声TOFD检测图像分割[J].机械工程学报,2011,47(8):35-40.
[99] Anelli M, Cinque L, Sangineto E. Deformation tolerant generalized Hough transformfor sketch-based image retrieval in complex scenes [J]. Image and Vision Computing,2007,25(11):1802-1813.
[100] Lo R C, Tsai W H. Gray-scale Hough transform for thick line detection in gray-scaleimages [J]. Pattern Recognition,1995,28(5):647-661.
[101] Song J Q, Lyu M R.. A Hough transform based line recognition method utilizing bothparameter space and image space [J]. Pattern Recognition,2005,38(4):539-552.
[102] Guo L F, Chutatape O. Influence of discretization in image space on Hough transforms[J]. Pattern Recognition,1999,32(4):635-644.
[103] Ding Y H, Ping X J, Hu M. Range image segmentation based on randomized Houghtransform [J]. Pattern Recognition Letters,2005,26(13):2033-2041.
[104] Shankar B U, Murthy C A, Pal S K. A new gray level based Hough transform forregion extraction: An application to IRS images [J]. Pattern Recognition Letters,1998,19(2):197-204.
[105]邱武,丁明跃,周华.基于实时灰度Hough变换的超声图像针状物体检测[J].计算机科学,2009,36(11):269-272.
[106] Yang R S H, Braden D R, Zhang G M. An automated ultrasonic inspection approachfor flip chip solders joint assessment [J]. Microelectronics Reliability,2012,52(12):2995-3001.
[107]李建民.接触聚焦超声波检测技术[M].北京：水利电力出版社,1992.
[108]赵荒培,王慈公,党政辉.大型铸锻件及结构件超声波探伤[M].北京：冶金工业出版社,2012.
[109] Chapman R K. A system model for the ultrasonic inspection of smooth planar cracks[J]. Journal of Nondestructive Evaluation,1990,9(2-3):197-210.
[110]牛俊民.钢中缺陷的超声波定性探伤[M].北京：冶金工业出版社,2012.
[111]张志永.水浸聚焦超声波探伤原理[M].北京：国防工业出版社,1985.
[112] Mallat S G. A theory for multiresolution signal decomposition: the waveletrepresentation [J]. Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
[113] Glowinski R, Lawton W, Ravachol M, et al. Wavelet solutions of linear and nonlinearelliptic, parabolic and hyperbolic problems in one space dimension [J]. ComputingMethods in Applied Sciences and Engineering,1990,55:203-210.
[114] Flandrin P, Magand F, Zakharia M. Generalized target description and waveletdecomposition [J]. IEEE Transactions on Acoustics, Speech, and Signal Processing,1990,38(2):350-352.
[115] Escudie B, Torresani B. Wavelet representation, time-scaled matched receiver forasymptotic sonar signals emitted by bats [J]. Signal Processing-Theories andApplications,1990,1:305-312.
[116] Resnikoff H L, Burrus C S. Relationships between the fourier transform and thewavelet transform [J]. The International Society for Optical Engineering,1990,1348:291-300.
[117] Barnes A E. Instantaneous frequency and amplitude at the envelope peak of aconstant-phase wavelet [J]. Geophysics,1991,56(7):1058-1060.
[118] Gorshkov V A. Studies of the relationship between the velocity propagation ofultrasonics and concrete strength by using a method of statistical simulation [J]. BetoniZhelezobeton,1972,11:21-23.
[119] Kagawa Y, Yamabuchi T. Finite element simulation of a composite piezoelectricultrasonic transducer [J]. IEEE Trans Sonics Ultrason,1979,26(2):81-88.
[120] Keenan D M, Shorter P A. Simulation by diffusion on a manifold with boundary:Applications to ultrasonic prenatal medical imaging [J]. SIAM Journal on AppliedMathematics,2004,64(3):932-960.
[121] Xu B Q, Ni X W, Shen Z H. Numerical simulation of laser-generated ultrasonic byfinite element method in the plate material [J]. Chinese Journal of Lasers,2004,31(5):621-625.
[122] Andrade J, Aniceto C, Patrocínio J, et al. Improving the quality of color imagesegmentation using genetic algorithm [J]. Lecture Notes in Computer Science,2013,8156(1):151-160.
[123] Liu F J, Cao B G.3D-modeling depth optimization using a neural network and imagesegments [J]. Applied Mechanics and Materials,2013,333:1096-1105.
[2]米谷弘.车体制造中的新型激光焊接技术[J].国外机车车辆工艺,2008,7(4):1-4.
[3]彭惠民.铁道车辆的激光焊接[J].铁道机车车辆工人,2008,8(8):32.
[4] EMMELMANN C. Introduction to indusial laser material processing [M]. Rofinsinar,2000.
[5] McNab A, Thomson J. An eddy current array instrument for application on ferriticwelds [J]. NDT&E International,1995,28(2):103-112.
[6] Rao B P C, Raj B, Jayakumar T, et al. An artificial neural network for eddy currenttesting of austenitic stainless steel welds [J]. NDT&E International.2002,35(2):393-398.
[7] Noritaka Y, Eiji M, Ladislav J, et al. Application of eddy current inversion technique tothe sizing of defects in Inconel welds with rough surfaces [J]. Nuclear Engineering andDesign.2005,235:1469-1480.
[8]姚立东,赖圣,付跃文,等.飞机多层金属结构脉冲涡流检测提离效应的抑制[J].失效分析与预防,2013,8(3):131-135.
[9] Babot D, Berodias G, Peix G. Detection and sizing by X-ray compton scattering ofnear-surface cracks under weld deposited cladding [J]. NDT&E International,1991,24(5):247-251.
[10]孙怡,孙洪雨,白鹏,等.射线焊缝图像中缺陷的实时检测方法[J].焊接学报,2004,22(2):115-118.
[11]陈明,马跃洲,陈光. X射线线阵实时成像焊缝缺陷检测方法[J].焊接学报,2007,28(6):81-84.
[12]丁克勤,陈光,张旭.基于线阵探测器的Ｘ和γ射线角焊缝检测试验分析[J].无损检测,2012,34(9):27-29.
[13] Ditchburn R J, Burke S K, Scala C M. NDT of welds: state of the art [J]. NDT&EInternational,1996,29(2):11l-l17.
[14] Hübschen G, Salzburger H J. Inspection of dissimilar metal welds using horizontallypolarized shear (SH-)waves and electromagnetic ultrasonic (EMUS-) probes [J].International Journal of Pressure Vessels and Piping,1989,39(4):331-344.
[15] Nichols R W, Crutzen S. Ultrasonic inspection of heavy section steel components: thePISC II final report [C]. London:1988.
[16] Crutzen S, Jehenson P, Nichols R, et al.‘PISC III: Status report and future trends’ in10th International Conference on NDE in the Nuclear and Pressure Vessel Industries
[C]. ASM International: Materials Park,1990.
[17] Chen Z H, Shi Y W, Jiao B Q, et al. Ultrasonic nondestructive evaluation of spot weldsfor zinc-coated high strength steel sheet based on wavelet packet analysis [J]. Journalof Materials Processing Technology,2009,209(5):2329-2337.
[18] Denisov A A, Shakarji C M, Lawford B B, et al. Spot weld analysis with2D ultrasonicarrays [J]. Journal of Research of the National Institute of Standards and Technology,2004,109:233-244.
[19] Maev R G, Ptchelintsev A, Denisov A A. Ultrasonic imaging with2D matrixtransducers [J]. Journal for Material Characterization,2000,3:326-332.
[20] Mozurkewich G, Ghaffari B, Potter T J. Spatially resolved ultrasonic attenuation inresistance spot welds [J]. Implications for Nondestructive Testing,2008,48:343-350.
[21] Siemiatko W, Chojecki, Rafal, et al. Mobile system for non destructive testing of weldjoints via time of flight diffraction (TOFD) technique [J]. Robot Motion and Control,2012,422:65-74.
[22] Ataby A, Nuaimy W, Brett C R. Automatic detection and classification of weld flawsin TOFD data using wavelet transform and support vector machines [J]. Insight,2010,52(11):597-602.
[23] Wang W B, Wang Y Q, Qing Z. Signal analysis of ultrasonic system with phased arrayand TOFD technique applied on inspecting pipeline welds defects [J]. EngineeringStructural Integrity: Research, Development and Application,2007, S1:1235-1238.
[24] Praveena A, Vijayarekhaa K, Saju T, et al. Signal quality enhancement using higherorder wavelets for ultrasonic TOFD signals from austenitic stainless steel welds [J].Ultrasonics,2013,53(7):1288-1292.
[25] Brekow G, Erhard A. Weld examination on tube test specimen using TOFD [J].Material Prufung,2000,42(11-12):437-443.
[26] Gang T, Xu Y, Chi D Z, et al. Ultrasonic TOFD testing for aluminum alloy weld ofthick plate [J]. Transactions of Nonferrous Metals Society of China,2005,15:79-82.
[27] Vicen R, Gil R, Jarabo P, et al. N-linear filtering of ultrasonic signals using neuralnetworks [J]. Ultrasonics,2004,42(1-9):355-360.
[28] Liu Z Q, Lu M D, Wei M. Structure noise reduction of ultrasonic signals usingartificial neural network adaptive filtering [J]. Ultrasonics,1997,35(4):325-328.
[29] Andria G, Attivissimo F, Giaquinto N. Digital signal processing techniques for accurateultrasonic sensor measurement [J]. Measurement,2001,30(2):105-114.
[30] Vijayalakshmi K, Muthupandi V, Jayachitra R. Influence of heat treatment on themicrostructure, ultrasonic attenuation and hardness of SAF2205duplex stainless steel[J]. Materials Science and Engineering: A,2011,529(25):447-451.
[31] Izquierdo M A G, Hernández M G, Anaya J J. Time-varying prediction filter forstructural noise reduction in ultrasonic NDE [J]. Ultrasonics,2006,44(22):1001-1005.
[32] Ka ys R, Svilainis L. Ultrasonic detection and characterization of delaminations inthin composite plates using signal processing techniques [J]. Ultrasonics,1997,35(5):367-383.
[33] Zhang G M, Zhang C Z. Sparse signal representation and its applications in ultrasonicNDE [J]. Ultrasonics,2012,52(3):351-363.
[34] Shakibi B, Honarvar F, Moles M D C, et al. Resolution enhancement of ultrasonicdefect signals for crack sizing [J]. NDT&E International,2012,52(11):37-50.
[35] Izquierdo M A G, Anaya J J, Mart n ez O. Multi-pattern adaptive inverse filter forreal-time deconvolution of ultrasonic signals in scattering media [J]. Sensors andActuators A: Physical,1999,76(1-3):26-31.
[36] Wei L, Huang Z Y, Que P W. Sparse deconvolution method for improving thetime-resolution of ultrasonic NDE signals [J]. NDT&E International,2009,42(5):430-434.
[37] Drai R, Khelil M, Benchaala A. Time frequency and wavelet transform applied toselected problems in ultrasonics NDE [J]. NDT&E International,2002,35(8):567-572.
[38] Rodr g uez M A, Lázaro J C, Ramos A. Ultrasonic flaw detection in NDE of highlyscattering materials using wavelet and Wigner-Ville transform processing [J].Ultrasonics,2004,42(1-9):847-851.
[39] Yang W X, Hull J B, Seymour M D. A contribution to the applicability of complexwavelet analysis of ultrasonic signals [J]. NDT&E International,2004,37(6):497-504.
[40] Song S P, Que P W. Wavelet based noise suppression technique and its application toultrasonic flaw detection [J]. Ultrasonics,2006,44:188-193.
[41] Praveen A, Vijayarekha K, Abraham S T. Signal quality enhancement using higherorder wavelets for ultrasonic TOFD signals from austenitic stainless steel welds [J].Ultrasonics,2013,53:1288-1292.
[42] Bettayeb F, Haciane S, Aoudia S. Improving the time resolution and signal noise ratioof ultrasonic testing of welds by the wavelet packet [J]. NDT&E International,2005,38,(6):478-484.
[43] Tadao O, Tadashi N, Akinori Y, Osamu S, Fumi F, Shuichi Y, Hiroshi H. Humanultrasonic hearing is induced by a direct ultrasonic stimulation of the cochlea [J].Neuroscience Letters,2013,539(28):71-76.
[44] Javadi Y, Akhlaghi M, Najafabadi M A. Using finite element and ultrasonic method toevaluate welding longitudinal residual stress through the thickness in austeniticstainless steel plates [J]. Materials&Design,2013,45(3):628-642.
[45] Ke W, Castaings M, Bacon C.3D finite element simulations of an air-coupledultrasonic NDT system [J]. NDT&E International,2009,42(6):524-533.
[46] Sadeghi S, Najafabadi M A, Javadi Y. Using ultrasonic waves and finite elementmethod to evaluate through-thickness residual stresses distribution in the friction stirwelding of aluminum plates [J]. Materials&Design,2013,52(12):870-880.
[47] Hosokawa A. Ultrasonic pulse waves in cancellous bone analyzed by finite-differencetime-domain methods [J]. Ultrasonics,2006,44(12):227-231.
[48] Vanhille C, Conde C, Campos-Pozuelo C. Finite-difference and finite-volume methodsfor nonlinear standing ultrasonic waves in fluid media [J]. Ultrasonics,2004,42(1-9):315-318.
[49] Nguyen V H, Naili S, Sansalone V. Simulation of ultrasonic wave propagation inanisotropic cancellous bone immersed in fluid [J]. Wave Motion,2010,47(2):117-129.
[50] Doyle P A, Scala C M. Near-field ultrasonic Rayleigh waves from a laser line source[J]. Ultrasonics,1996,34(1):1-8.
[51] Tan A, Hirose S, Wang C Y. A2D time-domain BEM for transient wave scatteringanalysis by a crack in anisotropic solids [J]. Engineering Analysis with BoundaryElements,2005,29(6):610-623.
[52] Li H L, Chong C P, Chan H L W. Finite element analysis on1-3piezocomposite ringsfor ultrasonic transducer applications [J]. Ceramics International,2004,30(7):1827-1830.
[53] Zhang J Y, Xu W J, Carlier J. Modelling and simulation of high-frequency (100MHz) ultrasonic linear arrays based on single crystal LiNbO3[J]. Ultrasonics,2012,52(1):47-53.
[54]施义茂,张建寰,陈仲怀.超声相控阵探头声场优化设计仿真.厦门大学学报(自然科学版),2013,52(3):370-375.
[55]肖砚,王强,胡栋.复杂工件内部缺陷超声柔性探头检测数值仿真[J].湖南科技大学学报(自然科学版),2013,27(4):65-68.
[56] Baek E, Yim H. Numerical modeling and simulation for ultrasonic inspection ofanisotropic austenitic welds using the mass-spring lattice model [J]. NDT&EInternational,2011,44(7):571-582.
[57] Castagnede B, Aknine A, Melon M. Ultrasonic characterization of the anisotropicbehavior of air-saturated porous materials [J]. Ultrasonics1998,36(1-5):323-341.
[58] Nico F, Degrieck J, Leroy O. Ultrasonic polar scans: Numerical simulation ongenerally anisotropic media [J]. Ultrasonics,2006,45(1-4):32-39.
[59] Schubert M, Grill W. Determination of the group and phase velocity surfaces ofacoustic waves in anisotropic materials by ultrasonic acoustic microscopy with phasecontrast [J]. Ultrasonics,1998,36(1-5):367-370.
[60] Kolkoori S, Hoehne C, Prager J. Quantitative evaluation of ultrasonic C-scan image inacoustically homogeneous and layered anisotropic materials using three dimensionalray tracing method [J]. Ultrasonics,2013,19:1-11.
[61] Kolkoori S R,. Rahman M U, Chinta P K. Ultrasonic field profile evaluation inacoustically inhomogeneous anisotropic materials using2D ray tracing model:Numerical and experimental comparison [J]. Ultrasonics,2013,53(2):396-411.
[62] Chassignole B, Guerjouma R E, Ploix M A. Ultrasonic and structural characterizationof anisotropic austenitic stainless steel welds: Towards a higher reliability in ultrasonicnon-destructive testing [J]. NDT&E International,2010,43(4):273-282.
[63] Apfe A, Moysan J, Corneloup G. Coupling an ultrasonic propagation code with amodel of the heterogeneity of multipass welds to simulate ultrasonic testing [J].Ultrasonics,2005,43:447-456.
[64] Eriksson A S, Mattsson J, Niklasson A J. Modelling of ultrasonic crack detection inanisotropic materials [J]. NDT&E International,2000,33(7):441-451.
[65] Ye J. Development of an ultrasonic ray model for phased array ultrasonic testing inanstenitic weldments [C].17thWorld Conference on Nondestructive Testing,2008.
[66] Liu Y, G X S, Zhang D, Gong X F. A simulation study of ultrasonic imaging ofmicro-crack in bone based on time-reversed approach [J]. Acta Acustica,2011,36(2):179-184.
[67] Zhou Y Q, Wang Z H, Ma H W. Numerical simulation of crack detection in pipes usingultrasonic longitudinal guided-wave [J]. Journal of North University of China (NaturalScience Edition),2006,27(3):258-262.
[68] Li Z Q, Lei J, Pan W F. Numerical simulation of cracks detection using ultrasonicwave [J]. Journal of Wuhan University of Technology,2006,28(10):52-54.
[69] He C F, Li W,Wu B. Numerical simulation of longitudinal crack detection in pipesusing ultrasonic guided wave T(0,1) mode [J]. Journal of Beijing University ofTechnology,2007,33(10):1009-1013.
[70] Mitsui H, Ebihara T, Mizutani K. Simulation of internal crack detection inshape-distorted cast billets using time-of-flight measurements of longitudinalultrasonic waves [J]. Materials Transactions,2012,53(2):337-341.
[71] Wu S J, Kuo I, Shung K K. Boundary element simulation of backscattering propertiesfor red blood with high frequency ultrasonic transducers [J]. Ultrasonics,2005,43(3):145-151.
[72] Wang L G, Deng J J, Shen J Z. A time-domain boundary element model for ultrasonicpulse echo [J]. Ultrasonics,1997,35(2):125-130.
[73]王路根,邓京军,沈建中.一种计算固体中超声脉冲散射声场的边界元法[J].声学学报,1997,22(1):28-32.
[74] Ohyama S, Takayama J, Watanabe Y. Temperature distribution and wind vectormeasurement using ultrasonic CT based on the time of flight detection [J]. Sensors andActuators A: Physical,2009,151(2):159-167.
[75] Shiels R, Leibel S, Order S, et al. The value of ultrasonic imaging and CT scanning inplanning the radiotherapy for prostatic carcinoma [J]. International Journal ofRadiation Oncology Biology Physics,1978,4(2):201.
[76] Zhu N, Jiang Y, Kato S. Ultrasonic computerized tomography (CT) for temperaturemeasurements with limited projection data based on extrapolated filtered backprojection (FBP) method [J]. Energy,2005,30(2-4):509-522.
[77] Sciubba D M, Burdette C, Pennant W A, et al. Percutaneous CT-Guided conformalultrasonic ablation of vertebral tumors using a rabbit tumor model [J]. Journal ofVascular and Interventional Radiology,2008,19(2):136-141.
[78] Weill F, Brun P, Rohmer P, et al. Migrations of fluid of pancreatic origin: ultrasonicand CT study of28cases [J]. Ultrasound in Medicine&Biology,1983,9(5):485-496.
[79] Saigusa H, Saigusa M, Aino I. M-Mode color doppler ultrasonic imaging of verticaltongue movement during articulatory movement [J]. Journal of Voice,2006,20(1):38-45.
[80] McHugh R, McDicken W N, Bow C R, et al. An ultrasonic pulsed doppler instrumentfor monitoring human fetal breathing in utero [J]. Ultrasound in Medicine&Biology,1978,3(4):381-384.
[81] Awrangjeb M, Lu G J. Techniques for efficient and effective transformed imageidentification [J]. Journal of Visual Communication and Image Representation,2009,20(8):511-520.
[82] Theodoropoulos G, Loumos V, Anagnostopoulos C. A digital image analysis and neuralnetwork based system for identification of third-stage parasitic strongyle larvae fromdomestic animals [J]. Computer Methods and Programs in Biomedicine,2000,62(2):69-76.
[83] Cécile B, Fran oise K S, Fran ois V D. Image processing to facilitate imageidentification by partially-sighted [J]. International Congress Series,2005,1282(9):980-984.
[84] Katayama T, Hirai T. Parameter identification for noisy image via the EM algorithm [J].Signal Processing,1990,20(1):15-24.
[85] Rahman Z. Jobson D J, Woodell G A. Investigating the relationship between imageenhancement and image compression in the context of the multi-scale retinex [J].Journal of Visual Communication and Image Representation,2011,22(3):237-250.
[86] Huang K Q, Wang Q, Wu Z Y. Natural color image enhancement and evaluationalgorithm based on human visual system [J]. Computer Vision and ImageUnderstanding,2006,103(1):52-63.
[87] Bismuth V, Vaillant R, Funck F, et al. A comprehensive study of stent visualizationenhancement in X-ray images by image processing means [J]. Medical Image Analysis,2011,15(4):565-576.
[88] Chen B, Latifi S, Kanai J. Edge enhancement of remote sensing image data in the DCTdomain [J]. Image and Vision Computing,1999,17(12):913-921.
[89] Chung K L, Yang W J, Yan W M. Efficient edge-preserving algorithm for colorcontrast enhancement with application to color image segmentation [J]. Journal ofVisual Communication and Image Representation,2008,19(5):299-310.
[90] Sikora T. Low Complexity shape-adaptive DCT for coding of arbitrarily shaped imagesegments [J]. Signal Processing: Image Communication,1995,7(4-6):381-395.
[91] Yi J W, Cho S J, Kim W J, et al. A new coding algorithm for arbitrarily shaped imagesegments [J].Signal Processing: Image Communication,1998,12(3):231-242.
[92] Koike K, Nagano S, Ohmi M. Lineament analysis of satellite images using a SegmentTracing Algorithm (STA)[J]. Computers&Geosciences,1995,21(9):1091-1104.
[93] Bigand A, Bouwmans T, Dubus J P. Extraction of line segments from fuzzy images [J].Pattern Recognition Letters,2001,22(13):1405-1418.
[94] Kim D S, Lee K H. Segment-based region of interest generation for pedestriandetection in far-infrared images [J]. Infrared Physics&Technology,2013,61:120-128.
[95] Pavlidis G, Tsompanopoulos A, Papamarkos N. A multi-segment image coding andtransmission scheme [J]. Signal Processing,2005,85(9):1827-1844.
[96] Thrift P R., Dunn S M. Approximating point-set images by line segments using avariation of the Hough transform [J]. Computer Vision, Graphics, and ImageProcessing,1983,21(3):383-394.
[97]刘海华,陈心浩,高智勇.基于形态学操作和模糊聚类技术的超声图像分割[J].电子学报,2007,35(7):1307-1312.
[98]盛朝阳,刚铁,迟大钊.基于分水岭方法的超声TOFD检测图像分割[J].机械工程学报,2011,47(8):35-40.
[99] Anelli M, Cinque L, Sangineto E. Deformation tolerant generalized Hough transformfor sketch-based image retrieval in complex scenes [J]. Image and Vision Computing,2007,25(11):1802-1813.
[100] Lo R C, Tsai W H. Gray-scale Hough transform for thick line detection in gray-scaleimages [J]. Pattern Recognition,1995,28(5):647-661.
[101] Song J Q, Lyu M R.. A Hough transform based line recognition method utilizing bothparameter space and image space [J]. Pattern Recognition,2005,38(4):539-552.
[102] Guo L F, Chutatape O. Influence of discretization in image space on Hough transforms[J]. Pattern Recognition,1999,32(4):635-644.
[103] Ding Y H, Ping X J, Hu M. Range image segmentation based on randomized Houghtransform [J]. Pattern Recognition Letters,2005,26(13):2033-2041.
[104] Shankar B U, Murthy C A, Pal S K. A new gray level based Hough transform forregion extraction: An application to IRS images [J]. Pattern Recognition Letters,1998,19(2):197-204.
[105]邱武,丁明跃,周华.基于实时灰度Hough变换的超声图像针状物体检测[J].计算机科学,2009,36(11):269-272.
[106] Yang R S H, Braden D R, Zhang G M. An automated ultrasonic inspection approachfor flip chip solders joint assessment [J]. Microelectronics Reliability,2012,52(12):2995-3001.
[107]李建民.接触聚焦超声波检测技术[M].北京：水利电力出版社,1992.
[108]赵荒培,王慈公,党政辉.大型铸锻件及结构件超声波探伤[M].北京：冶金工业出版社,2012.
[109] Chapman R K. A system model for the ultrasonic inspection of smooth planar cracks[J]. Journal of Nondestructive Evaluation,1990,9(2-3):197-210.
[110]牛俊民.钢中缺陷的超声波定性探伤[M].北京：冶金工业出版社,2012.
[111]张志永.水浸聚焦超声波探伤原理[M].北京：国防工业出版社,1985.
[112] Mallat S G. A theory for multiresolution signal decomposition: the waveletrepresentation [J]. Pattern Analysis and Machine Intelligence,1989,11(7):674-693.
[113] Glowinski R, Lawton W, Ravachol M, et al. Wavelet solutions of linear and nonlinearelliptic, parabolic and hyperbolic problems in one space dimension [J]. ComputingMethods in Applied Sciences and Engineering,1990,55:203-210.
[114] Flandrin P, Magand F, Zakharia M. Generalized target description and waveletdecomposition [J]. IEEE Transactions on Acoustics, Speech, and Signal Processing,1990,38(2):350-352.
[115] Escudie B, Torresani B. Wavelet representation, time-scaled matched receiver forasymptotic sonar signals emitted by bats [J]. Signal Processing-Theories andApplications,1990,1:305-312.
[116] Resnikoff H L, Burrus C S. Relationships between the fourier transform and thewavelet transform [J]. The International Society for Optical Engineering,1990,1348:291-300.
[117] Barnes A E. Instantaneous frequency and amplitude at the envelope peak of aconstant-phase wavelet [J]. Geophysics,1991,56(7):1058-1060.
[118] Gorshkov V A. Studies of the relationship between the velocity propagation ofultrasonics and concrete strength by using a method of statistical simulation [J]. BetoniZhelezobeton,1972,11:21-23.
[119] Kagawa Y, Yamabuchi T. Finite element simulation of a composite piezoelectricultrasonic transducer [J]. IEEE Trans Sonics Ultrason,1979,26(2):81-88.
[120] Keenan D M, Shorter P A. Simulation by diffusion on a manifold with boundary:Applications to ultrasonic prenatal medical imaging [J]. SIAM Journal on AppliedMathematics,2004,64(3):932-960.
[121] Xu B Q, Ni X W, Shen Z H. Numerical simulation of laser-generated ultrasonic byfinite element method in the plate material [J]. Chinese Journal of Lasers,2004,31(5):621-625.
[122] Andrade J, Aniceto C, Patrocínio J, et al. Improving the quality of color imagesegmentation using genetic algorithm [J]. Lecture Notes in Computer Science,2013,8156(1):151-160.
[123] Liu F J, Cao B G.3D-modeling depth optimization using a neural network and imagesegments [J]. Applied Mechanics and Materials,2013,333:1096-1105.