The paper explores Structural Health Monitoring (SHM) using ultrasonic guided waves, to detect structural damage. Guided waves are highly sensitive to changes in material properties and environmental and operating conditions (EOCs), with temperature being a significant factor. A key challenge in guided waves based SHM is differentiating between damage and temperature-induced changes. This paper focuses on warping-based methods for temperature compensation. Dynamic Time-Warping (DTW) encounters challenges due to its quadratic complexity. However, applying constraints to DTW accelerates the warping process by limiting the scope of the warping path to specified areas. The applied constraint should align with the characteristics of the signal. In this paper, we propose a global constraint for temperature compensation of guided waves, referred to as the Triangular global constraint (Tri-DTW). The performance of the proposed method will be compared with the Sakoe-Chiba global constraint (SC-DTW). Tri-DTW performs well, demonstrating better warping performance with four times reduced computational complexity. The analysis also includes comparisons of warping performance, warping performance with respect to temperature and damage detection performance.

Metal based additive manufacturing techniques such as laser powder bed fusion can produce parts with complex designs as compared to traditional manufacturing. The quality is affected by defects such as porosity or lack of fusion that can be reduced by online control of manufacturing parameters. The conventional way of testing is time consuming and does not allow the process parameters to be linked to the mechanical properties. In this paper, ultrasound data along with supervised learning is used to estimate the manufacturing parameters of 316L steel samples. The steel samples are manufactured with varying process parameters (speed, hatch distance and power) in two batches that are placed at different locations on the build plate. These samples are examined with ultrasound using a focused transducer. The ultrasound scans are performed in a dense grid in the build and transverse direction, respectively. Part of the ultrasound data are used to train a partial least squares regression algorithm by labelling the data with the corresponding manufacturing parameters (speed, hatch distance and power, and build plate location). The remaining data are used for testing of the resulting model. To assess the uncertainty of the method, a Monte-Carlo simulation approach is adopted, providing a confidence interval for the predicted manufacturing parameters. The analysis is performed in both the build and transverse direction. Since the material is anisotropic, results show that there are differences, but that the manufacturing parameters has an effect of the material microstructure in both directions.

Introduction: Guided waves being highly sensitive to temperature variations, temperature compensation algorithms, such as Optimal Baseline Selection, Baseline Signal Stretch and Scale Transform demonstrate effective performance under limited conditions. Dynamic Time Warping (DTW) has shown excellent compensation performance, however it comes with a substantial computational burden of O(N*N), where N represents the number of samples in each signal. Methodology: DTW works by construction of cost matrix that maps every point in one time series to all the points in the other time series, that results in complexity O(N*N).This problem can be solved by narrowing the search window using global constraints. The two most common constraints in the literature are the Sakoe-Chiba band and the Itakura Parallelogram. This paper uses Sakoe-Chiba band as a global constraint, the Sakoe-Chiba band is defined through a window size parameter which determines the largest temporal shift allowed from the diagonal. Temperature compensation performance of DTW Sakoe-Chiba is tested using the available OGW dataset #2 provided by Jochen Moll et al. The OGW dataset has been generated using 12 number of piezoelectric transducers bounded to CFRP (Carbon Fiber Reinforced Plastic) plate and varying the temperature from 20-60 degree with an increment of 0.5 degree. The signal is recorded for 1300 microseconds, that results in 13000 samples/time stamps(N). Initial results show that the Sakoe-Chiba constraint based DTW performs well and at a significantly lower cost, as indicated below. Result: The largest temporal shift (r) is estimated using Local Peak Coherence (LPC),for signal at 20 and 60 degree r is 135 samples. This results in complexity O(N*2r) which is very less than conventional DTW compensation technique O(N*N). In the final paper, the analysis will be replicated across a range of temperatures, and the performance of damage detection will be thoroughly discussed.

Metal based additive manufacturing techniques such as laser powder bed fusion (LPBF) can produce parts with complex designs as compared to traditional manufacturing. The quality is affected by defects such as porosity or lack of fusion that can be reduced by online control of manufacturing parameters. The conventional way of testing is time consuming and does not allow the process parameters to be linked to the mechanical properties. In this paper, ultrasound data along with supervised learning is used to estimate the manufacturing parameters of 316L steel cubes. Nine cubes with varying manufacturing parameters (speed, hatch distance and power) are examined with ultrasound using focused transducers. The volumetric energy density (VED) is calculated from the process parameters for each cube. The ultrasound scans are performed in a dense grid in the built and transverse direction. The ultrasound data is used in partial least square regression algorithm by labelling the data with speed, hatch distance and power and then by labelling the same data with the VED. These models are computed for both measurement directions and as the samples are anisotropic, we see different behaviours of estimation in each direction. The model is then validated with an unknown set from the same 9 cubes. The manufacturing parameters are estimated and validated with a good accuracy making way for online process control.

As the demand of structural integrity in manufacturing industries is increasing, the ultrasonic array technique has drawn more attention thanks to its inspection flexibility and versatility. By taking advantage of the possibility of individual triggering of each array element, full matrix capture (FMC) data acquisition strategy has been developed that contains the entire information of an inspection scenario. Total focusing method (TFM) as one of the ultrasonic imaging algorithms, is preferably applied to FMC dataset since it uses all information in FMC to synthetically focus the sound energy at every image pixel in the region of interest. Half-skip TFM (HSTFM) is proposed in multi-mode TFM imaging that involves a backwall reflection wave path, so that the defect profile could be reconstructed for accurate defect characterization. In this paper, a method involving Snell’s law-based wave mode conversion is proposed to account for more reasonable wave propagation time when wave mode conversion happens at backwall reflection in HSTFM. A series of model based simulations (in software simSUNDT) are performed for parametric studies, with the intention of investigating the capability of defect characterization using HSTFM with varying tilt angle and relative position of surface-breaking notch to array probe. The results show that certain TFM modes could help with defect characterization, but the effectiveness is limited with varying defect features. It is inappropriate to address a certain mode for all characterization perspectives but rather a combination, i.e., multi-mode TFM, should be adopted for possible interpretation and characterization of defect features.

The total focusing method (TFM) is a post-processing imaging technique applied on full matrix capture (FMC) ultrasonic inspection (UT) dataset. In TFM the ultrasonic wave energy is synthetically focused on every pixel in the image region of interest (ROI). In terms of half-skip TFM (HSTFM), wave mode conversion happens when the wave rebounds at interface, such as specimen backwall. This paper aims to propose and evaluate a method that involves Snell’s law to address accurate estimation of distanceof-flight (DOF) of wave propagation when wave mode conversion appears in HSTFM. This HSTFM algorithm is applied to both experimental and simulated FMC dataset that inspects a surface-breaking notch for notch image reconstruction. Comparisons between images with and without considering Snell’s law in wave mode conversion show visible difference that could lead to misinterpretations in characterizing the defect. The sensitivity of TFM to varying defect features such as defect tilt angle is also studied using simulated FMC datasets.

There are several freely available toolboxes for modeling the sound pressure field of ultrasound transducers and transducer arrays (e.g., Field II, k-Wave, and DREAM, etc.). These model the beam patterns, or how the ultrasound pulse changes depending on where we observe it, i.e., they model the spatial impulse response of the transducers. Normally, the transmitted pulse is not modeled using these toolboxes, but instead it is assumed that this pulse shape is known. Also, the models are based on assumption of an ideal behavior of the transducers, which is not necessarily the case for a real-world transducers. As a consequence, fitting these models to real measurement data, in order for them to mimic the individual transducer available in the lab, is not generally not possible with any numerical accuracy. In this paper we show, instead, how a deep learning approach can be adopted to train a model that with numerical accuracy models an transducer individual. We compare the proposed technique with real measurements and models using the Field II toolbox and show that for the actual transducer at hand, the deep learning approach outperforms the results from Field II.

Additive manufacturing is used to produce complex and tailored products that cannot be achieved using conventional manufacturing approaches. The products can be made from different materials including polymers, metals, etc. The material is added layer by layer to create a final product. The mechanical properties of the final part depend on the process parameters. To improve the quality of the product these manufacturing parameters need to be optimised and for this purpose machine learning along with ultrasound measurements can be used. In this paper, the manufacturing parameters of 50 mm thick polymer cubes are linked to the ultrasound data using partial least squares regression. Three cubes with varying layer heights are made from PLA and ABS each, and backscattered responses of ultrasound are recorded from these six cubes. The ultrasound data is used in the partial least squares algorithm to estimate the layer height and the filament type. The clusters that are formed using the first few components obtained from the algorithm show that the data points of the six cubes can be distinguished and themanufacturing parameters are estimated with good accuracy.

Estimation of frequency-dependent attenuation and speed of sound using ultrasound is of great importance. The acoustic properties can be used for material characterization and to study the local variations in a solid. As ultrasound is a mechanical wave, it is directly sensitive to changes in the material properties. The layered nature of additively manufactured prod-ucts pose a challenge for the estimation of acoustic properties. The non-parametric approaches using frequency transforms are sensitive to noise. In this paper, a parametric model is used to estimate the phase velocity and attenuation of 3D-printed cubes. The received signal from the cubes is a superposition of the backscattered responses from multiple layers of the printed part. A reference echo from aluminium is used as an input to the linear model and to estimate the received ultrasound response. The estimate of the ultrasound signal using the linear model is similar to the measured data and it suggests that it can be used to estimate wave propagation in additively manufactured products. The estimated acoustic properties show an increasing trend with the frequency and dispersion can be seen due to the layered nature of the material.