In order to achieve the non-destructive testing and quality evaluation of stainless-steel resistance spot welding (RSW) joints, a portable ultrasonic spiral C-scan testing instrument was developed based on the principle of ultrasonic pulse reflection. A mathematical model for the quality evaluation of RSW joints was established, and the centroid of the ultrasonic C-scan image in the nugget zone of the RSW was determined based on the principle of static moment. The longest and shortest axes passing through the centroid in the image were extracted, and the ratio of the longest axis to the shortest axis (RLS) factor and the average of axis (AOA) factor were calculated, respectively, to evaluate the quality of the joint. To study the effectiveness of the detection results, tensile tests, and stereo analysis were conducted on the solder joints after sampling. The results indicate that this detection method can realize online detection and significantly improve the detection efficiency; the detection value of internal defect size is close to the true value with an error of 0.1 mm; the combination of RLS and AOA factors can be used to evaluate the mechanical properties of RSW joints. This technology can be used to solve the NDT, evaluate problems of RSW joints, and realize engineering applications.

The escalating usage of paper cups and packaging materials with plastic coatings has evolved into a substantial environmental and health concern, evidenced by the report of microplastics in human blood. This research introduces an innovative laser-assisted thermal lens (TL) technique for the precise detection and measurement of microplastics, specifically those leaching from the inner plastic coatings of paper cups. Employing a multipronged approach encompassing scanning electron microscopy, optical microscopy, atomic force microscopy, Fourier transform infrared spectroscopy, UV-visible, and Raman spectroscopy, a comprehensive investigation is conducted into the leaching of microplastics into hot water from paper cups. The thermal diffusivity (D) of water samples containing microplastics is determined using the TL technique based on 120 observations for each temperature conducted using paper cups from three distinct manufacturers. The observation of a strong correlation between the number of microplastic particles (N) and D of the water sample enabled the setting of a linear empirical relation that can be used for computing the microplastics in water at a particular temperature. The study thus proposes a surrogate method for quantifying microplastics in water using the sensitive and non-destructive TL technique.

Ultrasonic bulk wave inspection of defects in safety-critical components with complex external geometries, such as turbine blades is challenging. While ultrasonic phased array imaging can yield high-resolution subsurface images, a commercial phased array probe can hardly be mounted on irregular external boundaries to perform in-situ imaging. In fact, a component with irregular shapes, as a highly reverberant body, is capable of generating elastic random diffuse or coda wavefields. The diffuse wavefields can be utilized to reconstruct Green\'s functions between any two passive receiving points. In this paper, an ultrasonic passive array imaging method using the diffuse reverberation resulting from complex boundaries is implemented to image internal defects. The method involves the utilization of active piezoelectric actuators to excite elastic diffuse waves within the component, which are received by a laser vibrometer scanning at multiple points. A passive full matrix capture (FMC) of array signals is extracted for defect imaging using the total focusing method. The proposed method is evaluated by the numerical simulations, and the effects of centre frequency, bandwidth, and source excitation methods on the imaging performance are investigated. An experiment using a turbine blade-like structure is conducted to further evaluate the imaging method.

Originating in the early 20th century, ultrasonic testing has found increasingly extensive applications in medicine, industry, and materials science. Achieving both a high signal-to-noise ratio and high efficiency is crucial in ultrasonic testing. The former means an increase in imaging clarity as well as the detection depth, while the latter facilitates a faster refresh of the image. It is difficult to balance these two indicators with a conventional short pulse to excite the probe, so in general handling methods, these two factors have a trade-off. To solve the above problems, coded excitation (CE) can increase the pulse duration and offers great potential to improve the signal-to-noise ratio with equivalent or even higher efficiency. In this paper, we first review the fundamentals of CE, including signal modulation, signal transmission, signal reception, pulse compression, and optimization methods. Then, we introduce the application of CE in different areas of ultrasonic testing, with a focus on industrial bulk wave single-probe detection, industrial guided wave detection, industrial bulk wave phased array detection, and medical phased array imaging. Finally, we point out the advantages as well as a few future directions of CE.

The use of Carbon Fibre Reinforced Plastic (CFRP) composite materials for critical components has significantly surged within the energy and aerospace industry. With this rapid increase in deployment, reliable post-manufacturing Non-Destructive Evaluation (NDE) is critical for verifying the mechanical integrity of manufactured components. To this end, an automated Ultrasonic Testing (UT) NDE process delivered by an industrial manipulator was developed, greatly increasing the measurement speed, repeatability, and locational precision, while increasing the throughput of data generated by the selected NDE modality. Data interpretation of UT signals presents a current bottleneck, as it is still predominantly performed manually in industrial settings. To reduce the interpretation time and minimise human error, this paper presents a two-stage automated NDE evaluation pipeline consisting of a) an intelligent gating process and b) an autoencoder (AE) defect detector. Both stages are based on an unsupervised method, leveraging density-based spatial clustering of applications with noise clustering method for robust automated gating and undefective UT data for the training of the AE architecture. The AE network trained on ultrasonic B-scan data was tested for performance on a set of reference CFRP samples with embedded and manufactured defects. The developed model is rapid during inference, processing over 2000 ultrasonic B-scans in 1.26 s with the area under the receiver operating characteristic curve of 0.922 in simple and 0.879 in complex geometry samples. The benefits and shortcomings of the presented methods are discussed, and uncertainties associated with the reported results are evaluated.

Additive manufacturing (AM) can create net or near-net-shaped components while simultaneously building the material microstructure, therefore closely coupling forming the material and shaping the part in contrast to traditional manufacturing with distinction between the two processes. While there are well-heralded benefits to AM, the widespread adoption of AM in fatigue-limited applications is hindered by defects such as porosity resulting from off-nominal process conditions. The vast number of AM process parameters and conditions make it challenging to capture variability in porosity that drives fatigue design allowables during qualification. Furthermore, geometric features such as overhangs and thin walls influence local heat conductivity and thereby impact local defects and microstructure. Consequently, qualifying AM material within parts in terms of material properties is not always a straightforward task. This article presents an approach for rapid qualification of AM fatigue-limited parts and includes three main aspects: (1) seeding pore defects of specific size, distribution, and morphology into AM specimens, (2) combining non-destructive and destructive techniques for material characterization and mechanical fatigue testing, and (3) conducting microstructure-based simulations of fatigue behavior resulting from specific pore defect and microstructure combinations. The proposed approach enables simulated data to be generated to validate and/or augment experimental fatigue data sets with the intent to reduce the number of tests needed and promote a more rapid route to AM material qualification. Additionally, this work suggests a closer coupling between material qualification and part certification for determining material properties at distinct regions within an AM part.

This paper introduces a methodology to recover the morphology of a complex rough surface from ultrasonic pulse echo measurements with an array of equidistant sensors using the one dimensional convolution neural network (1DCNN). The neural network is trained by the datasets simulated from high-fidelity finite element simulations for surfaces with a range of roughness parameters and is tested on both numerical and real experimental data. To assess the performance of our proposed method, the rough surface reconstruction results from the deep learning approach are compared with those obtained from conventional ultrasonic array imaging methods. Unlike array imaging-based methods that require a large number of sensors (e.g., 128, 64 or 32), the deep learning-based method uses pulse echo signals and can achieve accurate results with much fewer sensors. The developed deep learning approach has the potential to enable low-cost, accurate, and real-time reconstruction of complex surface profiles.

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.

This investigation explores the potential of electrochemical impedance spectroscopy (EIS) in evaluating graphene-based cementitious nanocomposites, focusing on their physical and structural properties, i.e., electrical resistivity, porosity, and fracture toughness. EIS was employed to study cement mixtures with varying graphene nanoplatelet (xGnP) concentrations (0.05-0.40% per dry cement weight), whereas flexural tests assessed fracture toughness and porosimetry analyses investigated the structural characteristics. The research demonstrated that the electrical resistivity initially decreased with increasing xGnP content, leveling off at higher concentrations. The inclusion of xGnPs correlated with an increase in the total porosity of the cement mixtures, which was indicated by both EIS and porosimetry measurements. Finally, a linear correlation emerged between fracture toughness and electrical resistivity, contributing also to underscore the use of EIS as a potent non-destructive tool for evaluating the physical and mechanical properties of conductive nano-reinforced cementitious nanocomposites.

On-stream inspections are the most appropriate method for routine inspections during plant operation without undergoing production downtime. Ultrasonic inspection, one of the on-stream inspection methods, faces challenges when performed at high temperatures exceeding the recommended 52 °C. This study aims to determine the ultrasonic velocity and attenuation with known material grade, thickness, and temperatures by comparing theoretical calculation and experimentation, with temperatures ranging between 30 °C to 250 °C on low-carbon steel, covering most petrochemical equipment material and working conditions. The aim of the theoretical analysis was to obtain Young\'s modulus, Poisson\'s ratio, and longitudinal velocity at different temperatures. The experiments validated the theoretical results of ultrasonic change due to temperature increase. It was found that the difference between the experiments and theoretical calculation is 3% at maximum. The experimental data of velocity and decibel change from the temperature range provide a reference for the future when dealing with unknown materials information on site that requires a quick corrosion status determination.