• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1544-1553
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• 1999

Recently, ultrasonic testing techniques have been widely used in the evaluation of the quality of metal. In this experiment, six heat-treated temperature of specimen have been considered : 0, 1200, 1250, 1300, 1350 and 1387$^{\circ}C$. As heat-treated temperature increases, the grain size of stainless steel also increases and then, eventually make it destroy. In this paper, a pattern recognition method is proposed to identify the heat-treated temperature of metals by evidence accumulation based on artificial intelligence with multiple feature parameters; difference absolute mean value(DAMV), variance(VAR), mean frequency(MEANF), auto regressive model coefficient(ARC), linear cepstrum coefficient(LCC) and adaptive cepstrum vector(ACV). The grain signal pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. Especially ACV is superior to the other parameters. The results (96% successful pattern classification) are presented to support the feasibility of the suggested approach for ultrasonic grain signal pattern recognition.

• Journal of the Korean Society for Nondestructive Testing
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• v.10 no.1
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• pp.18-23
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• 1990

In materials with the large grain size, ultrasonic waves are highly attenuated by the grain boundary acting as scattering centers due to discontinuity of elastic constant. In this study, the improved probes were developed so that they minimized the effect of grain scattering in order to detect deep flaws in coarse grained materials. As the result, the developed ultrasonic transducers showed the better sensitivity and signal to noise ratio when compared with the commercial probes in testing the interior of coarse grained material.

• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.84-93
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• 1999

Determination of grain size of heat-treated stainless steel based fm digital ultrasonic signal processing technique is presented. This techniques consist in evidence accumulation with multiple feature parameters, difference absolute mean value(DAMV), variance(VAR), mean frequency (MEANF), auto regressive model coefficient(ARC) and linear cepstrum coefficient(LCC). Feature parameters were extracted from ultrasonic echo signal of heat-treated metals. It was found that a few parameters might not be sufficient to exactly evaluate the grain size of heat-treated metals. The determination of grain size of heat-treated metals was carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. In the work presented, heat-treated stainless steel samples with various grain sizes are examined. The processed experimental results supports the feasibility of the grain size determination technique presented.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.50-57
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• 1994

In the non-destructive evaluation techniques using ultrasonic signal, backscattering noise from grain interface decreases the SNR of received signal. In this paper, SSP(split-spectrum processing) based on the constant FBR decomposition method has been applied to enhance the SNR. This algorithm helps to find optimal parameters of filter bank through a simple theory and has an advantage that reduce the signal processing time compared with the conventional constant bandwidth decomposition method. In this experiment, the 304 stainless steel sample is heat-treated and received ultrasonic signal is processed by SSP using the constand bandwidth decomposition method and the constand FBR decomposition method enhanced the SNR by 1.4 dB and reduced the required number of filters by 4 compared with the constant bandwidth decomposition method.

• Journal of the Korean Society for Nondestructive Testing
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• v.7 no.2
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• pp.8-15
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• 1988

A split-spectrum processing technique for an ultrasonic flaw detection system has been developed, which improves the flaw-to-grain echo ratio in large-grained materials. The enhancement is achieved by partitioning a wide-band received spectrum to obtain frequency shifted bands, which are then processed to suppress the grain echoes with respect to the flaw echo, using a novel signal minimization algorithm. A technique for suppression of grain echoes has also been devised which takes advantage of the fact that the grain echo amplitude changes with the frequency of the incident ultrasound whereas the flaw echo amplitude does not. The combination of this technique and the new flaw detection system greatly improve the capabilities of ultrasonic evaluation of large grain materials.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.5
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• pp.455-463
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• 2003

Adaptive noise cancellation techniques are ideally suitable for reducing spatially varying noise due to the grain structure of material in ultrasonic nondestructive evaluation. Grain noises have an un-correlation property, while flaw echoes are correlated. Thus, adaptive filtering algorithms use the correlation properties of signals to enhance the signal-to-noise ratio (SNR) of the output signal. In this paper, a multi-stage adaptive noise cancellation (MANC) method using adaptive least mean square error (LMSE) filter for enhancing flaw detection in ultrasonic signals is proposed.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.4
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• pp.372-379
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• 2003

The problems encountered by ultrasonic testing of austenitic stainless steel weld joints are discussed in the paper. Due to low thermal conductivity and the occurrence of single phase between the melting point and the room temperature, coarse and oriented grains are formed in such weld metals more in thick sections. This leads to higher scattering at the grain boundaries and low signal to noise ratio, and extensive beam skewing. Experimental results to understand these problem are explained.

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.45-55
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• 1999

In this paper, we used the WLMS(Wavelet domain Least Mean Square) adaptive filter based on the wavelet transform to cancel grain noise. Usually, grain noise occurs in changes of the crystalline structure of metals in high temperature environment. It makes the detection of flaw difficult. The WLMS adaptive filtering algorithm establishes the faster convergence rate by orthogonalizaing the input vector of adaptive filter as compared with that of LMS adaptive filtering algorithm in time domain. We implemented the WLMS adaptive filter by using the delayed version of the primary input vector as the reference input vector and then implemented the CA-CFAR(Cell Averaging- Constant False Alarm Rate) threshold estimator. CA-CFAR threshold estimator enables to detect the flaw and back echo signals automatically. Here, we used the output signals of adaptive filter as its input signal. To Cow the statistical characteristic of ultrasonic signals corrupted by grain noise, we performed run test. The results showed that ultrasonic signals are nonstationary signal, that is, signals whose statistical properties vary with time. The performance of each filter is appreciated by the signal-to-noise ratio. After LMS adaptive filtering in time domain, SNR improves to about 2-3㏈ but after WLMS adaptive filtering in wavelet domain, SNR improves to about 4-6㏈.

• Journal of the Korean Society for Nondestructive Testing
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• v.16 no.1
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• pp.1-9
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• 1996

Many signal-processing techniques have been found to be useful in ultrasonic and nondestructive evaluation. Among the most popular techniques are signal averaging, spatial compounding, matched filters and homomorphic processing. One of the significant new process is split-spectrum processing(SSP), which can be equally useful in signal-to-noise ratio(SNR) improvement and grain characterization in several specimens. The purpose of this paper is to explore the utility of SSP in ultrasonic NDE. A wide variety of engineering problems are reviewed, and suggestions for implementation of the technique are provided. SSP uses the frequency-dependent response of the interfering coherent noise produced by unresolvable scatters in the resolution range cell of a transducer. It is implemented by splitting the frequency spectrum of the received signal by using gaussian bandpass filter. The theoretical basis for the potential of SSP for grain characterization in SUS 304 material is discussed, and some experimental evidence for the feasibility of the approach is presented. Results of SNR enhancement in signals obtained from real four samples of SUS 304. The influence of various processing parameters on the performance of the processing technique is also discussed. The minimization algorithm, which provides an excellent SNR enhancement when used either in conjunction with other SSP algorithms like polarity-check or by itself, is also presented.

• The Journal of the Acoustical Society of Korea
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• v.14 no.6
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• pp.27-31
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• 1995

In this paper, the determination method of available bandwidth for automation of the split-spectrum processing(SSP) has been studied. The SSP is used for the visibility enhancement of the ultrasonic signal with grain noise. Even though the SSP has proved useful in signal-to-noise ratio enhancement, its application and automation have been limited due to ambiguity in the determination of available bandwidth. Until recently, it is the usual practice to optimize the available bandwidth by trial and error. The spectral histogram is the statistical distribution of the spectral windows that is selected by the minimization algorithm with the whole band of the spectrum of the received ultrasonic signal. Since the available bandwidth can be determined adaptively using spectral histogram, this method can be used for automation of the SSP. In order to evaluate the determination technique of the available bandwidth using spectral histogram, this method is applied to experimental ultrasonic data. The experimental results show that the spectral histogram is an efficient method for determination of the available bandwidth and automation of the SSP.