RESEARCH ON AUTOMATIC INSPECTION TECHNIQUE OF REAL-TIME RADIOGRAPHY FOR TURBINE-BLADE

Transcription

1 RESEARCH ON AUTOMATIC INSPECTION TECHNIQUE OF REAL-TIME RADIOGRAPHY FOR TURBINE-BLADE Z.G. Zhou, S. Zhao, ad Z.G. A School of Mechaical Egieerig ad Automatio, Beijig Uiversity of Aeroautics ad Astroautics, Beijig 00083, P. R. Chia Abstract: To ispect turbie blade automatically, with a real-time radiographic system based o X-ray flat pael detector, computerized defect extractio techique is studied o the basis of characteristics of turbie blade s digital radiographic images. At first, i the light of a variety of gray-level i a turbie blade s digital radiographic image, it is divided ito six subareas. A adaptive media filter is used to smooth defects i each subarea. The, the filtrated image is subtracted from the raw image ad a differece image with flat backgroud ad outstadig defects is obtaied. After that, thresholdig is applied to the differece image ad defects i the turbie blade become obvious. Later o, a morphological opeig is used to realize oise reductio. I order to esure the accuracy of defects, a regio growig method is adopted to recostruct the defects. Fially, the feature data of defects are extracted. The compariso betwee computerized feature extractio results ad huma iterpretatio results idicates that the method metioed above is effective ad efficiet, which will lay a good foudatio for automatic ispectio of turbie-blade with X-ray. Keywords: real-time radiography; self-adaptive media filterig; defect extractio; image processig; odestructive testig Itroductio: As a traditioal ispectio techique of odestructive testig for idustrial equipmets ad compoets, the huma iterpretatio of radiographic films ca locate ay cavities, iclusios, porosities, etc, which may have bee formed durig the maufacturig or machiig process exactly or directly []. But this is a hard ad difficult task whe a great umber of defects are to be couted ad calibrated. Radiographic testig with film is also a expesive ad time-cosumig techique (exposure time ad developmet of the film). It is kow that several experts do ot have the same opiio o a give film, ad eve the same expert might have a differet report at the begiig or the ed of a workday. With the developmet of computer techology, image processig ad patter recogitio techology, some attempts have bee made to automate the ispectio process with computer. At the ed of 990s, the successful applicatio of X-ray flat pael detector i real time imagig system made it possible to acquire digital images with high resolutio. These ispectio images ca be processed directly with computer, which establishes a basis for itelliget recogitio of ispectio images of importat parts i aeroautic ad astroautic devices. I this paper, the possibilities are ivestigated of automatic defects extractio of X-ray ispectio images of turbie blade acquired with real time imagig system based o Flat Pael Detector, ad the method is researched of extractig defect feature by aalyzig the characteristics of ispectio images of turbie blade to solve the coflict betwee precisio of defects ad processig speed ad provide exact data to itelliget recogitio of defects. Aalysis of Digital Radiography (DR) Image of Turbie Blade: Figure is a DR image of a turbie blade acquired with a real time imagig system based o flat pael detector. Figure 2 is a magified part of the image of the turbie blade that cotais defects. Characteristics of a turbie blade image is as follows: ) Cotrast is low; 2) Edges of defects are blur (there are two rectagle-shaped defects i Figure 2); 3) There is a gray-level wave alog differet directio i differet area, such as a vertical gray-level wave i rabbet (Area A i Figure 3) ad a horizotal ad slopig gray-level wave i turbie blade body (Area D ad E i Figure 3); 4) Defects ad its backgroud have the same gray-level sometimes; 5) There are high gray-level defects (such as porosities ad flaws) ad low gray-level defects (such as matel iclusios ).

2 A B C a) 2-D image D E Fig. Ispectio image of turbie blade a) 3-D image Fig.2 Image of a part of defective turbie blade F Fig.3 Subarea of turbie blade Solutio: From aforemetioed aalysis, gray-level variety i a turbie blade s DR image is complicated ad the chagig directio of the gray-level of image backgroud is differet i differet areas. Accordigly, elimiatig backgroud was adopted to extract defects. I accordace with the feature of gray-level wave, the turbie blade area of the ispectio image is divided ito six subareas. I each subarea, at first, a media filter based o sca-lie is applied to smooth the high frequecies of the image (defects ad oise) while preservig the low frequecies. The, the filtrated image is subtracted from the raw image to retrieve the high frequecies without the low oes. After that, a global threshold is applied to separate defects ad backgroud. Fially, the feature data of defects is extracted. Realizig Steps: ) Edge Extractio. There are a turbie blade s image ad a white backgroud i the ispectio image. Defects oly exist i the turbie blade s image area. Cotour of a turbie blade is extracted by edge detectio. Restrictig the processig field i the turbie blade image area by the cotour ca decrease processig time. 2) Gray-level Ehacemet. The gray-level of the turbie blade s image area i raw ispectio image is low, its dyamic rage is arrow ad cotrast is poor. Therefore, the grey-levels must be stretched to the whole dyamic rage to improve quality of the image. The followig Formula () is applied to ehace the gray-levels. cx x [0, a] y = 255 ac () ( x 255) x ( a,255] 255 a Where x is gray-level before ehaced, y is gray-level after ehaced, c is a coefficiet ad c>, parameter a is threshold of the ispectio image. 3) Backgroud Elimiatio. A filter is applied to the ehaced image to smooth the defects. The the filtrated image is subtracted from the ehaced image ad a differece image with outstadig defects is obtaied. May kids of filters ca be used, but media filter ca be operated simply ad rapidly, ad it is self-adaptive easily. Whe a oe-dimesio media filter is used, ot oly are the defects with high gray-level smoothed, but also the defects with low gray-level are smoothed. Mea ad morphologic filters ca t achieve the same smoothig effects as media filter does. I order to smooth defects exactly ad rapidly, subarea ad adaptive media filterig are put forward. The turbie blade image area is divided ito six fields as Figure 3. Media filterig based o sca-lie

3 alog differet directio is applied i each subarea: horizotal filterig is applied i subarea A, B ad C, vertical filterig i subarea D ad F, ad filterig alog with air exit hole i subarea E. Whe the sca-lie directio based media filter is applied, the filter legth is adjusted with the size of defects adaptively. This kid of media filterig ca smooth defects completely. If there is ot ay defect i a sca-lie, o filter is applied. The procedure to determie the legth of filter is illumiated i Figure 4. At first, search the extremum poits cx of gray-level curve i a sca lie. The search the earest trough leftwards to lx ad rightwards to rx ; work out gray-level differece t betwee cx ad lx, ad t2 betwee cx ad rx : t = gray(cx) gray( lx), t 2 = gray(cx) gray( rx). Suppose t Defect is a threshold to distiguish backgroud ad defects, t Noise is a threshold to distiguish defects ad system oise. If tdefect mi( t, t 2 ) tnose legth of filter is 2 ( rx lx) +, otherwiselegth of filter is 0. Above metioed researchig cycle is repeated from rx to the ed of the sca lie ad the logest filter is applied to the whole sca lie. I order to process cross-subarea defects correctly, firstly, search the last extremum poit of the previous sca lie from the startig poit of the curret sca lie, ad determie the legth of filter; the process the curret sca lie startig from the extremum poit to the ed of curret sca lie; fially, search the first extremum poit of the ext sca lie from the ed poit of curret sca lie ad determie the legth of filter. Subtract the filtrated image from the ehaced image to obtai the differece image. Figure 5(a) ad (b) show the result, from that it ca be see that the backgroud is flat ad the defects are outstadig. 4) Thresholdig. Accordig to histogram of differet images, select a proper threshold to determie each pixel of the image whether it belogs to defects or backgroud, ad produce a correspodig biary image. Selectio of threshold is crucial to segmet defects. If threshold is too high, more defect pixels are judged as backgroud. Cotrarily, the result is just reverse. This will affect the shape ad size of segmeted defects. The backgroud of the differece image is flat so that it is possible to apply a global threshold to separate defects from backgroud. This gives a biary image. Threshold value based o gray-level histogram ca be applied to determie the threshold value exactly (such as Maxetropy [2] ). 5) Noise Reductio. After thresholdig, defects are separated from backgroud, but oises are separated with defects ievitably. The biary image is filtrated by morphological opeig operatio with 3+3 structurig elemet, which ca protect defects [3]. 6) Defects Growth. The area of the defects ca be affected by the oise reductio step. To improve the detectio, a regio growig method is applied with respect to ehaced image. There are may growth criteria, oe of the simpler methods is compariso of the grey-levels. A brief descriptio of defects growth is give below. 6.) Markig. 4 eighborhoods markig algorithm is applied to the biary image (the positio of 4 eighborhoods is show i Figure 6) to sca the image pixel to pixel ad a sequece umber to every pixel are obtaied. Suppose the gray-level of the backgroud is 0, gray-level of defects is 255, ad f (x, is the gray-level of curret pixel, l(x, is the sequece umber of curret pixel, f (i) are gray-level of eighborhood pixels of curret pixel ad l(i) is the sequece umber of them, where i =~4. The algorithm of 4 eighborhoods markig is described as followig. () Suppose label = 0 ; (2) Sca biary image from up to bottom ad from left to right. If f ( x, = 255, ivestigate the 4 eighborhood pixelsa) If there is ot ay defect pixel i the 4 eighborhoodsmark a ew sequece umber to the curret pixel, that is label = label +, l ( x, = label.b) If there is oly oe defect pixel i the 4 eighborhoods, mark the same sequece umber to the curret pixel as the defect pixel, that is l ( x, = l( i). c) If there are more tha oe defect pixels i the 4 eighborhoodsgive the miimum sequece umber of the 4 eighborhoods to the curret pixel, that is l (x, = mil( i).

4 t t 2 a) 2-dimesioal display of differece image b) 3-dimesioal display of differece image Fig.4 The gray-level curve of sca lie AB Fig. 5 Defect image after backgroud elimiatio ( x, y ) ( x, y ) ( x +, y ) ( x, ( x, Fig.6 Positio of 4 eighborhood pixels Fig. 7 Grow defects 6.2) Neighbor regio uitig. Uite eighborig regios with differet sequece umber ito oe regio ad make all pixels i oe regio have the same sequece umber. 6.3) Origial growig regio. Work out the cetre ad origial eclosig rectagle of defect fields by makig use of the sequece umber of defects. If the cetre is iside the defect, the cetre ad its 8 eighborhoods costruct the origial growig regio, otherwise, the origial growig regio is determied i accordace with the cetre ad the shape of the defect, ad it is located i the cetral sectio of defect at full steam. 6.4) Defect growig. Begi with origial growig regio. After several eclosig rectagle growig cycle [4], the defect is grow agai. The grow defects are show i Figure 7. 7) Feature extractio. The extracted feature parameters as follows: 7.) Cetre of gravity M ( x 0, y0) : x0 = xi y0 = yi, Where is the umber of pixels i a defect, ( x i, yi ) is the coordiate of defect pixel; 7.2) Area A: the umber of pixels i a defect. 7.3) Log diameter L ad short diameter S: the legth ad width of eclosig rectagle with the miimum area of the defect regio. 7.4) Perimeter P: track the boudary of defect usig 8 eighborhood algorithm [5] ad record the boudary iformatio data i freema directioal chai-code (show i Figure 8) accordig to the tred of the boudary. Odd umber chai-code correspodig legth 2, eve umber correspodig legth, the sum of all chai-code legth of a boudary is perimeter, the formula of perimeter as follows: C P = ( ) i (2) 2 2 i= Where is the umber of pixels i a boudary, Ci is the directioal umber of chai-code0~7. The extracted feature data is show i table.

5 Table Feature data of defects correspodig to Fig.7 Number Log Short Ceter Perimeter Area of defect diameter Diameter 26, , ( x, Fig.8 Freema positio codig Coclusios: X-ray digital radiography ad itelliget recogitio of defects are basis of automatic ispectio. A effective method for automatic defect extractio i ispectio image of turbie blade is developed i the paper. The method put forward i the paper is effective ad ca solve the coflict betwee processig speed ad precisio of extracted defects. Some coclusios are gotte as follows: ) The method ca extract defect iformatio quickly ad accurately, ad solve the coflict betwee processig speed ad precisio of extracted defects. 2) The backgroud of defects is elimiated by adaptive media filterig, ad a regio growig method is applied to grow defects, which esures that the extracted defects are accurate i size ad shape. 3) The processig regio is limited iside the turbie blade area by extractig the cotour accurately, which decreases the processig time cosequetly. Refereces: [] Liu Dezhe, Moder X-ray ispectio techology[m]. Beijig: Chia stadard press (i Chiese), 999. [2] Pu T. A ew method for grey level picture thresholdig usig the etropy of the histogram[j].sigal Process,980,2(3) [3] Cui Yi. Image processig ad aalysis mathematical morphology method ad applicatio[m]. Beijig: Sciece press, 2000 (i Chiese). [4] Wu Li, Dai Mig, Li Ya. Weldig field extractio ad preservatio of defect shape i alumiium alloy weldig lie ispectig image. Trasactios of the Chia Weldig Istitutio[J], 200, 22(2):4.(i Chiese) [5] Li Yu, Bao Susu, Yag Lu. Selectio ad outside boudary trackig techiques of object area i biary image. Joural of Chia ormal uiversity (atural sciece editio)[j], 2000(3):2729. (i Chiese) Ackowledgemet: The work is supported by Natioal Natural Sciece Foudatio of Chia. Grated Number is