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This article describes a comprehensive quantitative analysis on effect of columnar grain orientation on ultrasonic plane wave energy reflection and transmission behaviour in acoustically anisotropic austenitic weld materials. The quantitative results are presented for following general interfaces (a) Isotropic-Anisotropic, (b) Anisotropic-Isotropic, (c) Fluid-Anisotropic, (d) Anisotropic-Fluid, (e) Anisotropic-Anisotropic, (f) Anisotropic-Free surface occur during the ultrasonic non destructive evaluation of austenitic weld materials. Explicit analytical expressions are presented for energy reflection and transmission coefficients at an interface between two arbitrarily oriented transversely isotropic materials. By applying explicit analytical expressions for energy reflection and transmission coefficients, numerical results are presented for several columnar grain orientations of the transverse isotropic austenitic weld material including both real and complex domain of the reflected and transmitted normal component of slowness vectors. Valid domains of incident wave vector angles, angular dependency of energy reflection and transmission coefficients and critical angles for reflected and transmitted waves are discussed. The existence of a reflected (or) transmitted second branch of quasi shear vertical waves and its consequence to the ultrasonic non destructive testing of austenitic weld materials are investigated. The presented comprehensive quantitative evaluation provides an overview on the effect of anisotropic properties on energy reflection and transmission coefficients in columnar grained austenitic weld materials. [ABSTRACT FROM AUTHOR]

Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated. [ABSTRACT FROM AUTHOR]

The Scaled Boundary Finite Element Method is known as an efficient method for the simulation of ultrasonic wave propagation. As to investigate acoustic wave behavior in case of fluid‐structure interaction, a fluid model is implemented in the SBFEM for prismatic structures. To omit coupling terms a displacement‐based formulation is used. Spurious modes, which occur in the solution, are suppressed using a penalty parameter. To verify this formulation dispersion curves obtained with Comsol Multiphysics are compared to results of SBFEM. The results of both methods are in very good agreement. [ABSTRACT FROM AUTHOR]

• Analyzing material properties using sound waves. • Elastic properties determined through advanced image analysis and neural networks. • Measure materials using phased-array technology. • Enhanced data techniques adding real-world artifacts into simulated data. • Adapting neural network visualization tools for image-to-regression problems. This article presents a method to use the dispersive behavior of ultrasonic guided waves and neural networks to determine the isotropic elastic constants of plate-like structures through dispersion images. Therefore, two different architectures are compared: one using convolutions and transfer learning based on the EfficientNetB7 and a Vision Transformer-like approach. To accomplish this, simulated and measured dispersion images are generated, where the first is applied to design, train, and validate and the second to test the neural networks. During the training of the neural networks, distinct data augmentation layers are employed to introduce artifacts appearing in measurement data into the simulated data. The neural networks can extrapolate from simulated to measured data using these layers. The trained neural networks are assessed using dispersion images from seven known material samples. Multiple variations of the measured dispersion images are tested to guarantee the prediction stability. The study demonstrates that neural networks can learn to predict the isotropic elastic constants from measured dispersion images using only simulated dispersion images for training and validation without needing an initial guess or manual feature extraction, independent of the measurement setup. Furthermore, the suitability of the different architectures for generating information from dispersion images in general and an image-to-regression visualisation technique, are discussed. [ABSTRACT FROM AUTHOR]