Deep Learning Based Inversion of Locally Anisotropic Weld Properties from Ultrasonic Array Data

The ability to reliably detect and characterise defects embedded in austenitic steel welds depends on prior knowledge of microstructural descriptors, such as the orientations of the weld’s locally anisotropic grain structure. These orientations are usually unknown but it has been shown recently that they can be estimated from ultrasonic scattered wave data. However, conventional algorithms used for solving this inverse problem incur a significant computational cost. In this paper, we propose a framework which uses deep neural networks (DNNs) to reconstruct crystallographic orientations in a welded material from ultrasonic travel time data, in real-time. Acquiring the large amount of training data required for DNNs experimentally is practically infeasible for this problem, therefore a model based training approach is investigated instead, where a simple and efficient analytical method for modelling ultrasonic wave travel times through given weld geometries is implemented. The proposed method is validated by testing the trained networks on data arising from sophisticated finite element simulations of wave propagation through weld microstructures. The trained deep neural network predicts grain orientations to within 3° and in near real-time (0.04 s), presenting a significant step towards realising real-time, accurate characterisation of weld microstructures from ultrasonic non-destructive measurements. The subsequent improvement in defect imaging is then demonstrated via use of the DNN predicted crystallographic orientations to correct the delay laws on which the total focusing method imaging algorithm is based. An improvement of up to 5.3 dB in the signal-to-noise ratio is achieved

Real-time super-resolution mapping of locally anisotropic grain orientations for ultrasonic non-destructive evaluation of crystalline material

Estimating the spatially varying microstructures of heterogeneous and locally anisotropic media non-destructively is necessary for the accurate detection of flaws and reliable monitoring of manufacturing processes. Conventional algorithms used for solving this inverse problem come with significant computational cost, particularly in the case of high-dimensional, nonlinear tomographic problems, and are thus not suitable for near-real-time applications. In this paper, for the first time, we propose a framework which uses deep neural networks (DNNs) with full aperture, pitch-catch and pulse-echo transducer configurations, to reconstruct material maps of crystallographic orientation. We also present the first application of generative adversarial networks (GANs) to achieve super-resolution of ultrasonic tomographic images, providing a factor-four increase in image resolution and up to a 50% increase in structural similarity. The importance of including appropriate prior knowledge in the GAN training data set to increase inversion accuracy is demonstrated: known information about the material's structure should be represented in the training data. We show that after a computationally expensive training process, the DNNs and GANs can be used in less than 1 second (0.9 s on a standard desktop computer) to provide a high-resolution map of the material's grain orientations, addressing the challenge of significant computational cost faced by conventional tomography algorithms

Feasibility study of residual stress measurement using phased ‎array ultrasonic method

Residual stress measurement using the ultrasonic method is based on the acoustoelasticity law, which ‎states that the Time-of-Flight (ToF) of an ultrasonic wave is affected by the stress field. Traditionally, single-element ultrasonic transducers are used for residual stress measurement. In ‎this paper, a Phased Array Ultrasonic Testing (PAUT) system is used and the single ‎element transducers are replaced by 5 MHz and 10 MHz arrays with 8 and 16 elements, respectively. The 10 MHz transmitter array can generate 16 ultrasonic waves ‎and each of them can be received by any of the 16 elements of the 10 MHz receiver array. ‎Therefore, a matrix of 16 × 16 acoustic paths can potentially be generated. Each of these 256 LCR paths ‎is different from the others (i.e., different distance or different position of the travel path in the ‎material) whereby 256 ToFs can be generated. This is anticipated to increase the measurement ‎accuracy in comparison with the traditional setup in which only two acoustic paths can be generated by using three single element transducers. In this paper, a feasibility study is ‎conducted to investigate the requirements of a residual stress measurement system using the PAUT method. An advanced processing algorithm is also developed to analyse Full Matrix ‎Capture (FMC). Based on the preliminary results, some variations between different acoustic paths are ‎measured which prove that the effect of the residual stress on the ultrasonic wave is detectable using ‎the PAUT system.‎ Furthermore, the potential of this system for robotic residual stress measurement is discussed

Application of the factorisation method to limited aperture ultrasonic phased array data

This paper puts forward a methodology for applying the frequency domain Factorisation Method to time domain experimental data arising from ultrasonic phased array inspections in a limited aperture setting. Application to both synthetic and experimental data is undertaken and a multi-frequency approach is explored to address the difficulty encountered in empirically choosing the optimum frequency at which to operate. Additionally, a truncated singular value decomposition (TSVD) approach is implemented in the case where the flaw is embedded in a highly scattering medium, to regularise the scattering matrix and minimise the contribution of microstructural noise to the final image. It is shown that when the Factorisation Method is applied to multi-frequency scattering matrices, it can better characterise crack-like scatterers than in the case where the data arises from a single frequency. Finally, a volumetric defect and a lack-of-fusion crack are both successfully reconstructed from experimental data, where the resulting images exhibit only 3\% and 10\% errors respectively in their measurement

An analytical approach to objectively sizing cracks using ultrasonic phased array data

Ultrasonic phased array systems are becoming increasingly popular as tools for the nondestructive evaluation of safety-critical structures. The data captured by these arrays can be analysed to extract information on the existence, location and shape of defects. However, many of the existing imaging algorithms currently used for this purpose are heavily reliant on the choice of threshold at which the defect measurements are made and this aspect of subjectivity can lead to varying defect characterisations between different operators. To combat this, the work presented here uses the Born approximation to derive a mathematical expression for the crack size given the width of the pulse-echo response lobe of the frequency domain scattering matrix. These scattering matrices can be easily extracted from experimental data if the location of the flaw is known a priori and so the method has been developed exclusively for the objective characterisation of flaws. Due to the analytical nature of this work, conclusions can be drawn on the formula's sensitivity to various experimental parameters and these are corroborated using synthetic data. The sizing of a subwavelength crack is undertaken and it is shown that examination of the scattering matrix correctly captures the crack form of the defect and outperforms the standard TFM in this regard (the nature of the defect is obscured by side lobes in the TFM image). It is also suggested that the derived formulae could potentially be used to inform and optimise array design

Using laboratory experiments to develop and test new Marchenko and imaging methods

The Marchenko redatuming method estimates surface-to-subsurface Green’s functions. It has been employed to diminishthe effects of multiples in seismic data. Several such methods rely on an absolute scaling of the data; this is usually considered to be known in synthetic experiments, or is estimated using heuristic methods in real data. Here, we show using real ultrasonic laboratory data that the most common of these methods may be ill suited to the task, and that reliable ways to estimate scaling remains unavailable. Marchenko methods which rely on adaptive subtraction may therefore be more appropriate. We present two adaptive Marchenko methods: one is an extension of a current adaptive method, and the other is an adaptive implementation of a non-adaptive method. Our results show that Marchenko methods improve imaging compared to reverse-time migration, but less so than expected. This reveals that some Marchenko assumptions were violated in our experiment and likely are also in seismic data, showing that laboratory experiments contribute critical information to the development and testing of Marchenko-based methods