Abstract

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.

Highlights

  • Ultrasonic non-destructive evaluation (NDE) is widely used across a number of industries including aerospace, nuclear and oil and gas

  • We 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

  • We show that after a computationally expensive training process, the deep neural networks (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

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Summary

Introduction

Ultrasonic non-destructive evaluation (NDE) is widely used across a number of industries including aerospace, nuclear and oil and gas. An image of the component’s interior is generated via post-processing of this data to aid in the detection of any internal defects [42]. Mathematics and Statistics, University of Strathclyde, Glasgow, UK. Engineering Mathematics, University of Bristol, Bristol, UK within NDE typically assume that the material that is being inspected is isotropic and homogeneous. Conventional ultrasonic imaging algorithms which assume homogeneity or isotropy can fail to focus the energy correctly in the image domain in such cases and are unreliable [44, 55, 66]. Algorithms which incorporate a priori information about a material’s spatially varying properties significantly improve the accuracy of defect characterisation [55]

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