A MEMS Transducer for Ultrasonic Flaw Detection

Ultrasonic flaw detection is the most commonly used method in rail defect detection, and the defect judgment in flaw detection data is the most important link. The B-scan data of ultrasonic flaw detection vehicle is used for data analysis and recovery. A set of rail defect identification model based on BP neural network is established according to the channel distribution characteristics, digital combination characteristics and the arrangement and combination characteristics in time sequence of the recovered data. This method takes the 1–6 channel detection data as an example, and trains the model by extracting the rail head nuclear defect as the sample and making the training set, Experiments show that the model can effectively identify the defect data by setting a suitable threshold value, and give the location of the rail defect, help the flaw detection workers to improve the efficiency of rail defect detection, greatly reduce the labor intensity, and avoid the missed judgment of the rail defect.

The performance of an ultrasonic flaw detection system is valued by its success in differentiating flaw echoes from those scattered by microstructures (e.g. grain scattering or clutter). In order to successfully detect and classify the target echoes from background noise, an effective feature extracting method and a robust decision process are required. In this study, we present a comparative evaluation of three orthogonal signal decomposition methods: constant B (bandwidth); constant E (energy); and constant Q (i.e., ratio of bandwidth to center frequency), spanning the entire frequency band of the signal. An efficient method of implementing orthogonal decomposition using the Haar filter is presented. Orthogonal decomposition of the signal offers feature vectors suitable for classification and signal evaluation. The differences observed in the probability density function of clutter and the flaw echoes resulting from orthogonal decomposition are utilized to design the Bayes and fuzzy discriminant classifiers for flaw detection. These classifiers show good sensitivities in detecting flaw echoes in the presence of strong clutter where the signal-to-noise ratio is about zero dB. In this paper we present a mathematical derivation for these techniques and experimental results to demonstrate their application in ultrasonic nondestructive testing.

Ultrasonic flaw detection in cast iron by boxcar integration

The selection of an ultrasonic phased array inspection process has a great impact on the inspection results. For specific workpieces and processes, CIVA simulation can be used to quickly analyze the imaging effect, which is of great significance for process optimization. In this paper, CIVA software is used to carry out phased array detection simulation of defect holes in specific areas of phased array B-type test block, the same process is used to carry out actual phased array detection, and the positioning and quantitative accuracy of each defect hole is compared and analyzed. The results show that the difference between CIVA simulation and actual detection results is small, and the actual detection accuracy is slightly better than CIVA simulation.

The momentary contact method previously used for ultrasonic velocity measurements and flaw detection in hot specimens has been extended to the measurement of ultrasonic attenuation. In this method, a cool buffer rod is brought into contact with the hot specimen for a few milliseconds. A transducer on the cool end of the buffer rod transmits an ultrasonic pulse down the buffer rod and receives the echoes from the buffer/specimen interface and from the multiple reflections within the specimen. From the first three returning echoes, the attenuation in the specimen and the interface reflection coefficient are computed. Acoustic coupling at the interface is brought about by pressure alone for both longitudinal and shear waves without any bonding agent. The utility of the method is illustrated by measurements on SAE 52100 alloy steel in which the curie temperature was detected by a peak in attenuation in the ferromagnetic region followed immediately by a dip in attenuation in the paramagnetic region. At higher temperatures, grain growth is detected. [This work was supported by the Office of Naval Research, Code 468.]

Time-frequency techniques are applied for mixing signals from a material inspected by a multiple sensor detection system. Ultrasonic sensors are located at the perimeter of a rectangular shaped material evaluated in a pulse-echo scheme. The resulting mixed signal becomes a high-resolution time-frequency image of the material. Different kinds of classification techniques can be applied to this image in order to obtain the defects in the material. In this paper, a simulation and experimental evaluation of the proposed approach are presented. Several time-frequency transforms and the fuzzy c-means are used. In the simulations, backscattering of the material grain is modelled by using Gaussian and K distributions with different signal to noise ratio parameters. The validity of the presented method is assessed through the detection and spatial location of artificial defects in a material with a rectangular shape. The performance of the classification technique in discerning defects buried in the backscattering from the material grain microstructure, is also discussed.

Phased array ultrasonic testing, an effective ultrasonic testing (UT) technology, has been widely used in steel inspection because of its high accuracy, sensitivity, and efficiency. However, as its application in as-cast magnesium alloys has just begun, more research is needed. Considering the important role of the gain compensation in quantifying defects in magnesium alloys by ultrasonic phased array technology, the effects of microstructure, the position, size, and overlap of defects, and boundary distance (distance from the defect position to the side surface of the test casting) on gain compensation of as-cast AZ80 and AZ31 magnesium alloys were studied. Results show the gain compensation increases with the increase of grain size. There is a strict linear positive correlation between gain compensation and defect depth, but such relationship no longer exists due to the defects overlap, orientation and boundary distance. In addition, there is a strict linear negative correlation between the gain compensation and defect size.

Ultrasonic detection and characterization of flaws in coarse-grained materials exhibiting heterogeneous and scattering microstructure is of particular importance across many industries, but remains challenging. Most spectral based denoising methods in the literature are sensitive to material properties, which necessitate a troublesome parameter optimization process and consequently impede their application into ultrasonic image processing. In order to improve flaw visibility in an image, we propose a novel and robust clutter suppression method through spectral distribution similarity analysis (SDSA). This method isometrically segments all the time-series data in a dataset acquired by the Full-Matrix-Capture technique and then censuses the spectral distribution of global segments and of local segments for every focusing point in the Total-Focusing- Method image. The coefficient computed by measuring the similarity between the two spectral distributions reveals the possibility of a legitimate flaw indication. Experiments on two highly scattering samples were conducted to validate this method. By applying SDSA, crack visibility is greatly enhanced with an average >20 dB target-to-noise ratio enhancement for a stainless steel weld sample, whilst ∼30dB improvement for an austenitic steel sample. The proposed technique retains excellent performance for both samples when the selected segment length is varied, proving its robustness and highlighting its potential for application across various materials.

In this investigation, an adaptive filter algorithm using Fractional Fourier transform (FrFT) has been developed for enhancement of targets in microstructure noise. In particular, the FrFT is introduced as a transformation tool to prepare ultrasonic signals for a more compact support. It also reduces the non-stationarity of signals. Furthermore, a normalized least mean square (NLMS) filter is applied on the transformed signal to suppress the grain noise. In order to evaluate the performance of the algorithm, various experimental ultrasonic data sets are acquired from a large grained specimen with embedded defects. A 5-MHz broadband transducer is utilized for data acquisition. The proposed algorithm is applied to discriminate defects in presence of high grain scattering noise. The experimental study shows that the algorithm greatly reduces grain noise and improves the signal-to-grain-noise ratio by about 12 dB. With the help of FrFT and the NLMS filter, the algorithm successfully identifies defects embedded in grain noise. This type of study can broaden the scope of applying adaptive filter in material evaluation and target detection.

The key features of the piezoelectric materials for ultrasonic transducers are considered. A procedure is described for the fabrication of porous piezoelectric ceramics. It is shown that, owing to their high piezoelectric performance and ease of fabrication, porous ceramics may compete with lead niobate and piezoelectric composites in the fields of medical diagnostics and ultrasonic flaw detection.

Background/Objectives: The use of simple radio-technical processing of signals – synchronous detection – in the ultrasonic testing of products with large frequency-dependent attenuation of ultrasound is considered. Methods: The use of radar in the ultrasonic flaw detection methods allows for control of objects with a high level of attenuation of ultrasonic vibrations in a material. Findings: It is demonstrated that synchronous detection increases the accuracy of echo signal temporal position measurement and increases the reliability of US testing in the presence of narrowband interference and the correlation with the probing signal of the electroacoustic pickup. Improvements: The use of synchronous detection will significantly increase the sensitivity and noise immunity of the ultrasound equipment used to control the complex structural materials with high attenuation of ultrasound, such as concrete, cast iron, plastic, etc.

Discussion: “Ultrasonic Flaw Detection in Pipes by Means of Shear Waves” (Moriarty, C. D., 1951, Trans. ASME, 73, pp. 225–229)

Present work is a reporting of the case study for damage assessment of wind turbine tower, caused by accidental fire of the power cable in the tower of 2.2 MW wind turbine at a wind farm site in India. This work reports the methodology adopted for investigating the effect of thermal cycles caused due to accidental fire on the tower structure. Non-destructive testing methods such as, ultrasonic flaw detection, hardness testing and in-situ metallography is used for assessment of the fire affected components of tower structure of wind turbine. Appropriate interpretation of test results enabled us to recommend replacement of the damaged components of the wind turbine tower, while the unaffected components of tower are found safe to use. Further, it is observed that localized heating has affected the weld seam and plates of shell in some selected area only.

The new processing algorithm for wideband signals, which are propagating in frequency dispersion media, is developed in the context of the ground penetrating radar technology. The mathematical substantiation of offered method is presented on the base of linear functional spaces. The method is described for calculation acceleration on the base of the recursive approach. Results of numerical modeling of this method are presented for electromagnetic waves propagating in frequency dispersion media at utilization of chirp pulse signals. The Debye model is used as the model of electrical medium properties. The frequency dispersion of losses in the medium is taken into consideration at modeling. Results of operation modeling of the ground penetrating radar are described. Results of this paper will be useful for the ground penetrating radar technology and the ultrasonic flaw detection.

Fiber Bragg grating sensor is widely used in military, construction, transportation, aviation and other fields due to its advantages in high sensitivity, high precision, high multiplexing and small volume. However, in some special fields such as ultrasonic flaw detection, high-speed vibration and aeroengine monitoring, the signals are rapidly changing, thus requiring high speed sampling. But the demodulation speed of traditional fiber Bragg grating demodulation techniques is hardly to satisfy the requirements, which seriously limits the application of fiber Bragg grating sensor in these fields. To solve this problem, in this paper we propose a dispersion compensation fiber(DCF)-single mode fiber(SMF) dual-channel demodulation method. Based on the SMF and the DCF with the characteristics of positive and negative dispersion coefficients in the anomalous dispersion region respectively, and combining with the optical time domain reflection technology, high speed and high precision demodulation of fiber grating can be realized. This system adopts the whole fiber structure without wavelength scanning, and the grating wavelength and position information can be obtained according to the pulse delay difference under a single optical pulse. There are three factors that quite influence the system accuracy and need to be solved: the grating space disturbance which is caused by the temperature change of the sensor network fiber; the dual-channel length disturbance caused by the DCF-SMF dual-channel temperature change; the dispersion disturbance caused by the inaccurate dispersion difference of the DCF-SMF. By constructing the DCF-SMF dual-channel, adopting the reference grating and introducing the dispersion difference correction model, these influence factors are solved. The case of temperature disturbance elimination is tested by the 5-75℃ temperature experiments. And the results are as follows: when the temperature of the sensor network fiber changes, the standard deviation of this dual-channel demodulation system is 16.8 pm, while only using the DCF single-channel to form the demodulation system, the standard deviation is 3614 pm. And when the DCF-SMF dual-channel is disturbed by temperature, the standard deviation is 11.9 pm. For a long time demodulation under constant temperature, the standard deviation of this system is 6.4 pm. Thus the influences of the sensor network fiber temperature change and the dual-channel temperature change on the system demodulation accuracy are effectively reduced. The feasibility and accuracy of this method are also verified by the strain experiment. Experimental results show that the highest demodulation rate of this method is 1 MHz, while the linearity can be up to 0.9998, and the accuracy is about 8.5 pm. So the system with the dispersion difference correction model has a high precision. Therefore, this novel demodulation method has advantages of high speed and high precision, good stability and large dynamic range, and it is very applicable to quasi-distributed fiber Bragg grating sensing system.