In order to improve NDE reliability, it is important to recover as much as possible of the useful information in NDE waveforms. An on-line minicomputer is ideally suited to both the collection of data and the performance of sophisticated signal processing tasks. Using a variety of signal processing techniques, including windowing, self-normalization (of transducer properties and far-field diffraction effects), transformations (Fourier magnitude and phase transforms, autocorrelations, cepstra), feature extraction and pattern· recognition, it has been possible to obtain information about very small defects, strength of adhesive bonds and acoustic emissions which are not available by conventional means. Examples of these various capabilities are given

Elsley, R. K.

English

In order to improve NDE reliability, it is important to recover as much as possible of the useful information in NDE waveforms. An on-line minicomputer is ideally suited to both the collection of data and the performance of sophisticated signal processing tasks. Using a variety of signal processing techniques, including windowing, self-normalization (of transducer properties and far-field diffraction effects), transformations (Fourier magnitude and phase transforms, autocorrelations, cepstra), feature extraction and pattern· recognition, it has been possible to obtain information about very small defects, strength of adhesive bonds and acoustic emissions which are not available by conventional means. Examples of these various capabilities are given.</p

Elsley, R.

Digital Repository @ Iowa State University (ISU)

COMPUTER AIDED INTERPRETATION OF NDE SIGNALS R. K. Elsley Science Center, Rockwell International Thousand Oaks, California 91360 ABSTRACT In order to improve NDE reliability, it is important to recover as much as possible of the useful information in NDE waveforms. An on-line minicomputer is ideally suited to both the collection of data and the performance of sophisticated signal processing tasks. Using a variety of signal processing techniques, including windowing, self-normalization (of transducer properties and far-field diffraction effects), transformations (Fourier magnitude and phase transforms, autocorrelations, tepstra), feature extraction and pattern· recognition, it has been possible to obtain information about very small defects, strength of adhesive bonds and acoustic emissions which are not available by conventional means. Examples of these various capabilities are given. In the past, the ultrasonic signals from non-destructive testing apparatus have been. processed in only one simple way: the waveforms are recti-fied and converted into a "video" signal and dis-played on an oscilloscope. A peak in this video signal is taken to be an indication of a defect at the location corresponding to the arrival time of the signal, and the amplitude of the peak is taken as a measure of the defect's size. This method ignores a great deal of the in-formation present in the ultrasonic signals: no use is made of variations in the frequency content of the signals, phase relationships between parts of the signal are ignored, and long duration sig-nals, such as an echo train .from a layered struc-ture, are not treated as one signal. In order to use more of the information pres-ent, first, ultrasonic RF waveforms have to be captured; second, preprocessing operations which enhance the usable information have to be per-formed, and third, features of the signals which best predict defect type and size have to be deter-mined. Figure 1 shows a l~boratory minicomputer-based signal processing facility. The entire RF waveform is recorded digitally, and the computer performs a wide variety of signal processing operations using Rockwell's interpretive signal processor language. Figures 2 and 3 sbow how the properties of the particular ultrasooic transducer and asso-ciated electronics used can be removed from the signal in order to allow direct comparisons be-tween experiments, standards and theoretical predictions. In particular, Fig. 3 shows that some signals contain the information needed to "se 1 f-norma 1 i ze" themselves. Figure 4 shows that the use of a simple gate to pick out a part of a signal can be improved upon by using instead a window of carefully selected shape. Figure 5 shows that the size .of defects no larger than one wavelength of sound can be accu-326 rately determined by looking at appropriate prop-erties of the signals. Figure 6 shows the results of automatic pattern recognition experiments in which the com-puter learned what features of acoustic emission signals are indicative of what is going on within a part. Finally, Fig. 7 lists the areas in which the computer system has made contributions to non-destructive evaluation research. By using more of the information present in ultrasonic signals, it is now possible to answer questions about defects that it was not possible to answer before. Acknowledgement This research was sponsored by the Center for Advanced NDE operated by the Science Center, Rockwell International, for the Advanced Research Projects Agency and the Air Force Materials Laboratory under contract F33615-74-C-5180. lii!UACTIV£ DISPLAY AHO COI!Tl!!)L 11111111111111111 DISK l TAPE MASS STOM&E Figure 1. Laboratory minicomputer-based signal processing facility. WHEN MAK lNG BROADBAND SPEClRAL MEASUREMENTS, THERE ARE 2 DISTINCT FREQUENCY-DEPENDENT EFFECTS ASSOCIATED WITH THE TRANSDUCER. 1. RESONANT £LECTROMECHANICAL RESPONSE OF TRANSDUCER AND PULSER: NOT MUCH ENERGY AT VERY LOW OR HIGH FREQUENCIES. 2. DIFFRACTION DUE TO FINITE SIZ£ OF TRANSDUCER: lOW FREQUENCIES ARE IN FAR FIELD, HIGH FREQUENCIES ARE IN NEAR FIELD. US lNG A REFERENCE WAVEFORM (e. q. FROM A REFERENCE BlOCK OR SACK SURFACE!, ONE CAN NORMALIZE OUT THE TRANSDUCER RESPONSE AND THE FAR FIElD DIFFRACIION. THE RAW lUNNORMALIZEDI SPECTRUM IS THEREBY CONVERTED TO A NORMALIZED SPECTRUM WITH FlAT FREQUENCY RESPONSE WHEREVER SUFFICIENT SOUND ENERGY IS PRESENT. DIFnACTlOH OF' SOUHO FJ:OH A TRHNSDUtEit: e e • e e • ON AXIS SOUHD PPESSU£' FOR ~ PlSTOH SOURCE .... FIELO FR£8 <JtHZ> FRtf {tiHZ> FR£0 i I'IHZ > Figure 2. Removing transducer effects I. 327 2 • 2 e ' . IN SOME CASES EACH ULTRASONIC WAVEFORM CAN NORMALIZE !i.e. CORRECTliTSELF. THIS REMOVES NOT ONLY lRANSDUCER AND PULSER PROPERTIES ATTHAT MOMENT, BUT ALSO TEST PART IRREGUlARITIES SUCH AS WARPAGE OR MISALIGNMENT ATTHAT POSITION ON THE PART. .IN PULSE-ECHO TESTING OF ADHESIVELY BONDED ALUMINUM STRUCTURES, THE FRONT SURFACE ECHO IS USED TO NORMALIZE ICORRECTJ THE ENTIRE WAVEFORM. RF liAVEFO~K llAHSOUC£1: A FROHT SURFACE ECHQ ~~ I '1 .RAW IUNCORRECTEOi FREQUENCY SPECTRA ARE VERY DIFFERENT FOR DIFFERENT !RANSOUCERS. f.!F H~VJEFQPI'I TPAHSOUCEJ e AFTER SELF-NORMALIZATION, SPEC!RA ARE IDENTICAL FROM I TO 18 MHz. ~Ffi'O!iT SVPFHCE EtHO e.e 8.8 ~AW SPECTPU!'t TPAHSOUC ESt 8 REO <I'IHZ > e.e ...... ' ~ TI!1E <USl FftEQ ( I"'HZJ figure 3. Removing transducer effects II. Self-normalization. 0.18 CH OIAHETER VO!O Ill TITAHIUI! 1.8 2.!1 3.8 TillE <US> 1.e z.e 3.8 TIHE <US) IF A IIFECT SIGNAL IS ACCOMPANIED BY COHEREJII GRAIN BOUNDARY SCATTERING, • THE lAnER WILL DISTORT THE FREQUENCY SPEC!Riftl WHEN A SUITABLY SHAPED WINDOW IS MULTIPUED BY THE WAVEFORM BEFORE • FREQUEIW:Y ANALYSIS, THE GRAIN NOISE IS REOUCED WITHOUT INTRODUCING R INGIN& INTO THE SPEC!RUM 4.9 0.1 4.8 SPECT.RUH - AFTER WINOOWIHG 4.1 0.1 2.1! '4.0 FREQ <KHZ l CALCULATED SPEC TRUll - *. 9S Cl'l OIA"ETER 1!110 2.0 4.0 FREQ OIHZ) Figure 4. Windowing to recover defects from grain noise. 328 te.e .. ' I 6.41 6.8 • SEVERAL TECHNIQUES HAVE BEEN DEVELOPED FOR ESTIMATING THE SIZE OF SMALL DEFECTS FOR VOID DEFECTS WHICH ARE ROUGHLY SPHERICAL, CHANGit>IG SIZE MERELY STRETCHES FREQUENCY SCALE. THUS ANY FEATURE OF fREQUENCY SPECTRUM, SUCH AS PEA~ POSITION, GIVES AN ESTIMATE OF OIAMET£R • MORE GENERALLY, SLOPE OF PHASE PART OF FREQUENCY SPECTRUM T£LLS ABOUT SIZE AS FOUOWS SLOPE OF PHASE GIVES ARRIVAL TIME OF PULSE AT HIGH FREQUENCY, SLOPE GIVES ARRIVAL TIME FROM FRONT OF DEFECT AT LOW FREQUENCIES, SLOPE GIVES ARRIVAL TIME FROM CENTER OF DEFECT THE DIFFEREWCE GIVES A MATERIAL IWOEPENDENT MEASURE OF SIZE FOR INCLUSIONS OBLAII SPHEROitl f )9 1~1 0 z 4 6 8 11) 12 fR(OU(NCY lt.\Hll ll!ll lrnl 600 Figure 5. Size of small defects. 1.) I.S ).!: ).!: 51.0 j:e lO ... ~ :E < < ;l ~ < ~ 13 !I! "' Q "' 1:1 !:! io.s Jo.s 0 z §! (a) (b) fREQUENCY !MHzl 41Xl~otrnOIAM lONCITUOINAl l))l.lfl'l OIAM. LONG ITUOitlAL -THEORY ----EXPERIMENT PHASE ICA!.CUW£01 fOR SPHERICAL CAVITIES WAV£L£NClH lcrnl 1.5 0!,----: FR[QUENCY fMHzl (c) (d) Figure 6. Results of automatic pattern recognition experiments. 329 10 10 l!: 52.o ... ~ I 0 l:j ii.O i LS ~ SLO ~ < ~ ~ "' !:! ~o.s l! I FREQUENCY !MHzl (e) (f) Figure 6. (Continued) THE MINICOMPUTER SIGNAL PROCESSING SYSTEM HAS BEEN APPLIED IN THE FOLLOWING AREAS: • ANALYZE BROADBAND DATA FROM SMALL VOIDS AND INCLUSIONS IN TITANIUM • CALCULATE THEORETICAL SOLUTIONS FOR SCATIERING FROM SMALL ·DEFECTS • ESTIMATE SIZE AND IMPEDANCE OF SMALL SCATTERERS • ANALYZE BROADBAND DATA FROM ADHESIVELY BONDED ALUMINUM PARTS • CALCULATE THEORETICAL PULSE ECHO DATA FOR ADHESIVELY BONDED PARTS • DETERMINE STRENGTH RELATED FEATURES OF ADHESIVE BOND SIGNALS • COLLECT AMPLITUDE~TIME DATA FOR EVERY ACOUSTIC EMISSION IN A TEST • COLLECT WAVEFORMS AND SPECTRA FOR MOST ACOUSTIC EMISSIONS IN A TEST • AUTOMATICALLY IDENTIFY VARIOUS TYPES OF ACOUSTIC EMISSION SPECTRA • MEASURE VELOCITY, THICKNESS AND ATTENUATION OF COMPOSITES • ANALYZE CLEAN SURFACES BY ELLIPSOMETRY, SURFACE POTENTIAL DIFFERENCE AND PHOTOELECTRON EMISSION • MEASURE BONDING Of VERY INHOMOGENEOUS SPACE SHUTTlE INSULATION TILES • DISPLAY CHLADNI FIGURES OF HONEYCOMB SANDWICH PANELS • SIGNAL AVERAGE N<II-CONTACT ELECTROMAGNETIC TRANSDIJCER WAVEFORMS Figure 7. Applications 330 

Computer Aided Interpretation of NDE Signals

Elsley, R

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=4500&amp;context=qnde

Computer Aided Interpretation of NDE Signals

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Digital Repository @ Iowa State University (ISU)

Digital Repository @ Iowa State University (ISU)