Effective Focal Area Dimension Optimization of Shear Horizontal Point-Focusing EMAT Using Orthogonal Test Method

To overcome the shortcomings of low energy conversion efficiency of electromagnetic acoustic transducers (EMATs), point-focusing shear horizontal (PFSH) wave EMAT is used to focus the wave energy into a specific area. Many factors will affect the capability of the focusing transducer, and in addition to considering the signal intensity, the detection accuracy is also required to be investigated. Specifically, to simplify the test process, we use the orthogonal test method to study the effect of different influence parameters on signal intensity and focal area dimensions. Seven factors are selected, and three results are determined in the test. Range analysis shows that for signal amplitude M , the top three impact factors are the coil width w , coil turns n , and focal length lF (equal to bandwidth factor α ). Moreover, magnet number m and frequency fc dominate the effective focal length lfd , and aperture angle θ determines the effective focal width wfd . To enable higher signal intensity and smaller focal area dimensions, it is necessary to consider various factors on the PFSH-EMAT focusing performance. The test’s signal intensity with optimized parameters’ combination at the focal point is nearly 144.42% higher than the average of all the tests, lfd decreased by 37.84%, and wfd decreased by 50.59%. The experiment also verified that focusing EMAT with optimized parameters has a better focusing performance

High-resolution non-destructive evaluation of defects using artificial neural networks and wavelets

This paper presents artificialneuralnetworks (ANN) and wavelet analysis as methods that can assist highresolutionof multiple defects in close proximity in components. Without careful attention to analysis, multiple defects can be mis-interpreted as single defects and with the possibility of significantly underestimated sizes. The analysis in this work focussed on A-scan type ultrasonic signal. Amplitudes corresponding to the sizes of two defects as well as the phase shift parameter representing the distance between them were determined. The results obtained demonstrate very good correlation for sizes and distances respectively even in cases involving noisy signal dat

Effects Of The Flash Welding Process On Mechanical And Microstructural Properties Of Structural Steel Joints Assessed Using Dest

ASTM A529 carbon¿manganese steel angle specimens were joined by flash butt welding and the effects of varying process parameter settings on the resulting welds were investigated. The weld metal and heat affected zones were examined and tested using tensile testing, ultrasonic scanning, Rockwell hardness testing, optical microscopy, and scanning electron microscopy with energy dispersive spectroscopy in order to quantify the effect of process variables on weld quality. Statistical analysis of experimental tensile and ultrasonic scanning data highlighted the sensitivity of weld strength and the presence of weld zone inclusions and interfacial defects to the process factors of upset current, flashing time duration, and upset dimension. Subsequent microstructural analysis revealed various phases within the weld and heat affected zone, including acicular ferrite, Widmanstätten or side-plate ferrite, and grain boundary ferrite. Inspection of the fracture surfaces of multiple tensile specimens, with scanning electron microscopy, displayed evidence of brittle cleavage fracture within the weld zone for certain factor combinations. Test results also indicated that hardness was increased in the weld zone for all specimens, which can be attributed to the extensive deformation of the upset operation. The significance of weld process factor levels on microstructure, fracture characteristics, and weld zone strength was analyzed. The relationships between significant flash welding process variables and weld quality metrics as applied to ASTM A529-Grade 50 steel angle were formalized in empirical process models

Ultrasonic signal detection and recognition using dynamic wavelet fingerprints

A novel ultrasonic signal detection and characterization technique is presented in this dissertation. The basic tool is a simplified time-frequency (scale) projection which is called a dynamic wavelet fingerprint. Take advantage of the matched filter and adaptive time-frequency analysis properties of the wavelet transform, the dynamic wavelet fingerprint is a coupled approach of detection and recognition. Different from traditional value-based approaches, the dynamic wavelet fingerprint based technique is pattern or knowledge based. It is intuitive and self-explanatory, which enables the direct observation of the variation of non-stationary ultrasonic signals, even in complex environments. Due to this transparent property, efficient detection and characterization algorithms can be customized to address specific problems. Furthermore, artificial intelligence can be integrated and expert systems can be developed based on it.;Several practical ultrasonic applications were used to evaluate the feasibility and performance of this technique. The first application was ultrasonic materials sorting. Dynamic wavelet fingerprints of echoes from the surface of different plates were generated and then used to successfully identify corresponding plates.;The second application was ultrasonic periodontal probing. The dynamic wavelet fingerprint technique was used to expose the hidden trend of the complex waveforms. Taking the manual probing data as gold standard , a 40% agreement ratio was achieved with a tolerance limit of 1mm. However, statistically, lack of agreement was found in terms of the limits of agreement of Bland and Altman.;The third application was multi-mode Lamb wave tomography. The dynamic wavelet fingerprint technique was used to extract arrival times of transmitted Lamb wave modes. The overall quality of the estimated arrival times was acceptable in terms of their regular distributions and discernable variation patterns that correspond to specific defects. The tomographic images generated from estimated arrival times were also fine enough to indicate different defects in aluminum plates.;The last application was ultrasonic thin multi-layers inspection. High precision and robustness of a dynamic wavelet fingerprint based algorithm was demonstrated by processing simulated ultrasonic signals. When applied to practical data obtained from a plastic encapsulated IC package, multiple interfaces in the package were successfully detected

A review of ultrasonic sensing and machine learning methods to monitor industrial processes

Supervised machine learning techniques are increasingly being combined with ultrasonic sensor measurements owing to their strong performance. These techniques also offer advantages over calibration procedures of more complex fitting, improved generalisation, reduced development time, ability for continuous retraining, and the correlation of sensor data to important process information. However, their implementation requires expertise to extract and select appropriate features from the sensor measurements as model inputs, select the type of machine learning algorithm to use, and find a suitable set of model hyperparameters. The aim of this article is to facilitate implementation of machine learning techniques in combination with ultrasonic measurements for in-line and on-line monitoring of industrial processes and other similar applications. The article first reviews the use of ultrasonic sensors for monitoring processes, before reviewing the combination of ultrasonic measurements and machine learning. We include literature from other sectors such as structural health monitoring. This review covers feature extraction, feature selection, algorithm choice, hyperparameter selection, data augmentation, domain adaptation, semi-supervised learning and machine learning interpretability. Finally, recommendations for applying machine learning to the reviewed processes are made

On-line Structural Integrity Monitoring and Defect Diagnosis of Steam Generators Using Analysis of Guided Acoustic Waves

Integrity monitoring and flaw diagnostics of flat beams and tubular structures was investigated in this research using guided acoustic signals. The primary objective was to study the feasibility of using imbedded sensors for monitoring steam generator and heat exchanger tubing. A piezo-sensor suite was deployed to activate and collect Lamb wave signals that propagate along metallic specimens. The dispersion curves of Lamb waves along plate and tubular structures were generated through numerical analysis. Several advanced techniques were explored to extract representative features from acoustic time series. Among them, the Hilbert-Huang transform (HHT) is a recently developed technique for the analysis of non-linear and transient signals. A moving window method was introduced to generate the local peak characters from acoustic time series, and a zooming window technique was developed to localize the structural flaws. The dissertation presents the background of the analysis of acoustic signals acquired from piezo-electric transducers for structural defect monitoring. A comparison of the use of time-frequency techniques, including the Hilbert-Huang transform, is presented. It also presents the theoretical study of Lamb wave propagation in flat beams and tubular structures, and the need for mode separation in order to effectively perform defect diagnosis. The results of an extensive experimental study of detection, location, and isolation of structural defects in flat aluminum beams and brass tubes are presented. The time-frequency analysis and pattern recognition techniques were combined for classifying structural defects in brass tubes. Several types of flaws in brass tubes were tested, both in the air and in water. The techniques also proved to be effective under background/process noise. A detailed theoretical analysis of Lamb wave propagation was performed and simulations were carried out using the finite element software system ABAQUS. This analytical study confirmed the behavior of the acoustic signals acquired from the experimental studies. The results of this research showed the feasibility of on-line detection of small structural flaws by the use of transient and nonlinear acoustic signal analysis, and its implementation by the proper design of a piezo-electric transducer suite. The techniques developed in this research would be applicable to civil structures and aerospace structures