Adapting the full matrix capture and the total focusing method to laser ultrasonics for remote non destructive testing

Laser Induced Phased Arrays (LIPAs) use post processing to focus and steer the laser generated and detected ultrasonic beam, synthesizing a phased array. The technique is broadband, non-contact, and couplant free, making LIPAs suitable for large stand-off distances, inspection of components of complex geometries and hazardous environments. This paper presents LIPAs synthesized by capturing the Full Matrix (FMC), at the nondestructive, thermoelastic regime. The Total Focusing Method (TFM) is used as the imaging algorithm, where the captured signals are summed with the appropriate time delay, in order to synthesize a focus at every point in the imaging area. The FMC and the TFM, are adapted to the needs of LIPAs in order to enable fast imaging and make more efficient use of the information in the data. Experimental results are presented from nondestructive, laser ultrasonic inspection of an aluminum sample with side drilled holes at depths varying between 10 and 20 mm from the surface

Accurate depth measurement of small surface-breaking cracks using an ultrasonic array post-processing technique

AbstractIn this paper, the half-skip configuration of the Total Focusing Method (TFM) is used to image and size surface-breaking cracks. The TFM is an ultrasonic array post-processing technique which is used to synthetically focus at every image point in a target region. This paper considers the case of inspecting for cracks which have initiated from the far surface of a parallel-sided sample using an array on the near surface. Typically, only direct ray paths between the array and image points are included in the TFM algorithm and therefore the image obtained for this case consists only of root and tip indications; no specular reflection from the crack faces is captured. The tip indication often has such a poor signal-to-noise ratio that reliable crack depth measurement is challenging. With the Half-Skip TFM, instead of using directly-scattered signals, the image is formed using ultrasonic ray paths corresponding to the ultrasound that has reflected off the back surface and has then undergone specular reflection from the crack face back to the array. The technique is applied to experimental and simulated array data and is shown to measure the depth of small cracks (depth <1mm) with greater reliability than methods which rely on tip diffraction

Full matrix capture and the total focusing imaging algorithm using laser induced ultrasonic phased arrays

Laser ultrasonics is a technique where lasers are used for the generation and detection of ultrasound instead of conventional piezoelectric transducers. The technique is broadband, non-contact, and couplant free, suitable for large stand-off distances, inspection of components of complex geometries and hazardous environments. In this paper, array imaging is presented by obtaining the full matrix of all possible laser generation, laser detection combinations in the array (Full Matrix Capture), at the nondestructive, thermoelastic regime. An advanced imaging technique developed for conventional ultrasonic transducers, the Total Focusing Method (TFM), is adapted for laser ultrasonics and then applied to the captured data, focusing at each point of the reconstruction area. In this way, the beamforming and steering of the ultrasound is done during the post processing. A 1-D laser induced ultrasonic phased array is synthesized with significantly improved spatial resolution and defect detectability. In this study, shear waves are used for the imaging, since they are more efficiently produced than longitudinal waves in the nondestructive, thermoelastic regime. Experimental results are presented from nondestructive, laser ultrasonic inspection of aluminum samples with side drilled holes and slots at depths varying between 5 and 20mm from the surface

Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities

The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator manually interpreting data with the support of rudimentary automation tools. Recently, many demonstrations of deep learning (DL) techniques that address individual NDE tasks (data pre-processing, defect detection, defect characterisation, and property measurement) have started to emerge in the research community. These methods have the potential to offer high flexibility, efficiency, and accuracy subject to the availability of sufficient training data. Moreover, they enable the automation of complex processes that span one or more NDE steps (e.g. detection, characterisation, and sizing). There is, however, a lack of consensus on the direction and requirements that these new methods should follow. These elements are critical to help achieve automation of ultrasonic NDE driven by artificial intelligence such that the research community, industry, and regulatory bodies embrace it. This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by DL methodologies. The review is organised by the NDE tasks that are addressed by means of DL approaches. Key remaining challenges for each task are noted. Basic axiomatic principles for DL methods in NDE are identified based on the literature review, relevant international regulations, and current industrial needs. By placing DL methods in the context of general NDE automation levels, this paper aims to provide a roadmap for future research and development in the area.Comment: Accepted version to be published in NDT & E Internationa

A probabilistic approach for the optimisation of ultrasonic array inspection techniques

AbstractUltrasonic arrays are now used routinely for the inspection of engineering structures in order to maintain their integrity and assess their performance. Such inspections are usually optimised manually using empirical measurements and parametric studies which are laborious, time-consuming, and may not result in an optimal approach. In this paper, a general framework for the optimisation of ultrasonic array inspection techniques in NDE is presented. Defect detection rate is set as the main inspection objective and used to assess the performance of the optimisation framework. Statistical modelling of the inspection is used to form the optimisation problem and incorporate inspection uncertainty such as crack type and location, material properties and geometry, etc. A genetic algorithm is used to solve the global optimisation problem. As a demonstration, the optimisation framework is used with two objective functions based on array signal amplitude and signal-to-noise ratio (SNR). The optimal use of plane B-scan and total focusing method imaging algorithms is also investigated. The performance of the optimisation scheme is explored in simulation and then validated experimentally. It has been found that, for the inspection scenarios considered, TFM provides better detectability in a statistical sense than plane B-scan imaging in scenarios where uncertainty in the inspection is expected

Potential and limitations of NARX for defect detection in guided wave signals

Previously, a nonlinear autoregressive network with exogenous input (NARX) demonstrated an excellent performance, far outperforming an established method in optimal baseline subtraction, for defect detection in guided wave signals. The principle is to train a NARX network on defect-free guided wave signals to obtain a filter that predicts the next point from the previous points in the signal. The trained network is then applied to new measurement and the output subtracted from the measurement to reveal the presence of defect responses. However, as shown in this paper, the performance of the previous NARX implementation lacks robustness; it is highly dependent on the initialisation of the network and detection performance sometimes improves and then worsens over the course of training. It is shown that this is due to the previous NARX implementation only making predictions one point ahead. Subsequently, it is shown that multi-step prediction using a newly proposed NARX structure creates a more robust training procedure, by enhancing the correlation between the training loss metric and the defect detection performance. The physical significance of the network structure is explored, allowing a simple hyperparameter tuning strategy to be used for determining the optimal structure. The overall detection performance of NARX is also improved by multi-step prediction, and this is demonstrated on defect responses at different times as well as on data from different sensor pairs, revealing the generalisability of this method

Independent trapping and manipulation of microparticles using dexterous acoustic tweezers

An electronically controlled acoustic tweezer was used to demonstrate two acoustic manipulation phenomena: superposition of Bessel functions to allow independent manipulation of multiple particles and the use of higher-order Bessel functions to trap particles in larger regions than is possible with first-order traps. The acoustic tweezers consist of a circular 64-element ultrasonic array operating at 2.35MHz which generates ultrasonic pressure fields in a millimeter-scale fluid-filled chamber. The manipulation capabilities were demonstrated experimentally with 45 and 90-lm-diameter polystyrene spheres. These capabilities bring the dexterity of acoustic tweezers substantially closer to that of optical tweezers