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Additive manufacturing (AM) is a rising technology bringing new opportunities for design and cost of manufacturing, compared to standard processes like casting and machining. Among the AM techniques, direct energy deposition (DED) processes are dedicated to manufacture functional metallic parts. Despite of their promising perspectives, the industrial implementation of DED processes is inhibited by the lack of structural health control. Consequently, non-destructive testing (NDT) techniques can be investigated to inspect DED-manufactured parts, in an online or offline manner. To date, most ultrasonic NDT applications to metallic AM concerned the selective laser melting process; existing studies tackling DED processes mainly compare various ultrasonic techniques and do not propose a comprehensive control method for such processes. Current researches in the GeM laboratory focus on a multi-sensor monitoring method dedicated to DED processes, with a structural health control loop included, in order to track defect formation during manufacturing. In this way, this paper aims to be a proof of concept and proposes a comprehensive control method that opens the way to in situ ultrasonic control for DED. In this paper, a control method using the phased array ultrasonic testing (PAUT) technique is particularly illustrated on wire-arc additive manufacturing (WAAM) components, and its applicability to laser metal deposition (LMD) is also demonstrated. A specific attention is given to the calibration method, towards a quantitative prediction of the size of the detected flaws. PAUT predictions are cross-checked thanks to X-ray radiography, which demonstrates that the PAUT method enables to detect and dimension defects from 0.6 to 1 mm for WAAM aluminum alloy parts. Then, an applicable scenario of inspection of a WAAM industrial and large-scale part is presented. Finally, perspectives for in situ and real-time application of the chosen method are given. This paper shows that real-time monitoring of the WAAM process is possible, as the PAUT method can be integrated in the manufacturing environment, provides relevant in situ data, and runs with computing times compatible with real-time applications.

This paper presents a novel approach for preload measurement of bolted connections, specifically tailored for offshore wind applications. The proposed method combines robotics, Phased Array Ultrasonic Testing (PAUT), nonlinear acoustoelasticity, and Finite Element Analysis (FEA). Acceptable defects, below a pre-defined size, are shown to have an impact on preload measurement, and therefore conducting simultaneous defect detection and preload measurement is discussed in this paper. The study demonstrates that even slight changes in the orientation of the ultrasonic transducer, the non-automated approach, can introduce a significant error of up to 140 MPa in bolt stress measurement and therefore a robotic approach is employed to achieve consistent and accurate measurements. Additionally, the study emphasises the significance of considering average preload for comparison with ultrasonic data, which is achieved through FEA simulations. The advantages of the proposed robotic PAUT method over single-element approaches are discussed, including the incorporation of nonlinearity, simultaneous defect detection and stress measurement, hardware and software adaptability, and notably, a substantial improvement in measurement accuracy. Based on the findings, the paper strongly recommends the adoption of the robotic PAUT approach for preload measurement, whilst acknowledging the required investment in hardware, software, and skilled personnel.

This paper presents an ultrasonic inspection methodology to improve the fitness-for-service (F.F.S) assessment of hydraulic turbine runners after manufacturing or during the in-service inspection. The improvement proposed here is to apply ultrasonic array inspection techniques with an emphasis on the total focusing method (TFM) to produce data compatible with fitness-for-service methodologies. Conventional ultrasonic inspection methods based on good workmanship are mandatory for manufacturing, and in-service inspections are generally limited to surface methods such as penetrant or magnetic testing. Our previous work found serious limitations with conventional ultrasonic testing (UT) applied to the high-stress area located in the welded joint between the blade and the band. Undetected flaws will likely remain in a weld after fabrication, which can reduce the component’s service life. Our work is centered on a real turbine runner using various ultrasonic array configurations to characterize detected flaws left after fabrication. According to an evaluation of a Francis turbine runner, our results suggest that a dedicated TFM transducer with passive axis focusing and encoded inspection results in a higher detection rate, more accurate flaw definition, and more accurate sizing for the fitness-for-service (F.F.S) assessment of hydraulic turbine runners.

This paper presents a study on residual stress measurement in wire-arc additively manufactured (WAAM) titanium samples using the non-destructive method of phased array ultrasonics. The contour method (CM) was used for the verification of the phased array ultrasonic results. This allowed for a comparison of measurement methods to understand the effects on the distribution of residual stress (RS) within Ti-6Al-4V samples and the effectiveness of measurement of residual stress using phased array ultrasonics. From the results of the experiments, the phased array ultrasonic data were found to be in good agreement with the CM results and displayed similar residual stress distributions in the samples. The results of the individual elements of the phased array were also compared and an improvement in accuracy was found. From per-element results, anomalies were found and could be mitigated with the ability to average the results by using phased array ultrasonics. Therefore, based on these results, there is a strong case for the benefits of using phased array ultrasonics as a method of residual stress measurement for WAAM Ti-6Al-4V components over other existing residual stress measurement techniques.

Recent advancements in machine learning (ML) have led to state-of-the-art performance in various domain-specific tasks, driving increasing interest in its application to non-destructive testing (NDT). Among NDT techniques, phased array ultrasonic testing (PAUT) is an advanced extension of conventional ultrasonic testing (UT). This article provides an overview of recent research advances in ML applied to PAUT, covering key applications such as phased array ultrasonic imaging, defect detection and characterization, and data generation, with a focus on multimodal data processing and multidimensional modeling. The challenges and pathways for integrating the two techniques are examined. Finally, the article discusses the limitations of current methodologies and outlines future research directions toward more accurate, interpretable, and efficient ML-powered PAUT solutions.

Combinative methodologies have the potential to address the drawbacks of unimodal non-destructive testing and evaluation (NDT & E) when inspecting multilayer structures. The aim of this study is to investigate the integration of information gathered via phased-array ultrasonic testing (PAUT) and pulsed thermography (PT), addressing the challenges posed by surface-level anomalies in PAUT and the limited deep penetration in PT. A center-of-mass-based registration method was proposed to align shapeless inspection results in consecutive insertions. Subsequently, the aligned inspection images were merged using complementary techniques, including maximum, weighted-averaging, depth-driven combination (DDC), and wavelet decomposition. The results indicated that although individual inspections may have lower mean absolute error (MAE) ratings than fused images, the use of complementary fusion improved defect identification in the total number of detections across numerous layers of the structure. Detection errors are analyzed, and a tendency to overestimate defect sizes is revealed with individual inspection methods. This study concludes that complementary fusion provides a more comprehensive understanding of overall defect detection throughout the thickness, highlighting the importance of leveraging multiple modalities for improved inspection outcomes in structural analysis.

The anisotropy and inhomogeneity exhibited by austenitic steel in welds poses a challenge to nondestructive testing employing ultrasonic waves, which is predominantly utilized for the inspection of welds in power plants. In this study, we assess the reliability of phased array ultrasonic testing (PAUT) by analyzing the flaw detection sensitivity of ultrasonic beams in anisotropic welds, based on the inspection conditions. First, we simulated the sectorial scan technique, frequently employed for the inspection of actual welds, while taking into account the ultrasonic wave mode, frequency, and shape and position of a flaw. Subsequently, we analyzed the flaw sensitivity by comparing A-scan signals and S-scan results. The sensitivity analysis results confirmed the detection of all flaws by considering at least two inspection methods based on the shape and position of the flaw. Furthermore, we verified our model by performing an experiment under the same conditions as the simulation and found that the results were in agreement. Hence, we find that the simulation modeling technique proposed in this study can be utilized to develop suitable inspection conditions, according to the flaw characteristics or inspection environment.

This paper presents a comprehensive study on automated defect detection in complex structures using phased array ultrasonic testing data, focusing on both traditional signal processing and advanced deep learning methods. As a non-AI baseline, the well-known signal-to-noise ratio algorithm was improved by introducing automatic depth gate calculation using derivative analysis and eliminated the need for manual parameter tuning. Even though this method demonstrates robust flaw indication, it faces difficulties for automatic defect detection in highly noisy data or in cases with large pore zones. Considering this, multiple DL architectures—including fully connected networks, convolutional neural networks, and a novel Convolutional Attention Temporal Transformer for Sequences—are developed and trained on diverse datasets comprising simulated CIVA data and real-world data files from welded and composite specimens. Experimental results show that while the FCN architecture is limited in its ability to model dependencies, the CNN achieves a strong performance with a test accuracy of 94.9%, effectively capturing local features from PAUT signals. The CATT-S model, which integrates a convolutional feature extractor with a self-attention mechanism, consistently outperforms the other baselines by effectively modeling both fine-grained signal morphology and long-range inter-beam dependencies. Achieving a remarkable accuracy of 99.4% and a strong F1-score of 0.905 on experimental data, this integrated approach demonstrates significant practical potential for improving the reliability and efficiency of NDT in complex, heterogeneous materials.

As a promising reference technique for non-destructive evaluation of both electrowelded and butt-fused polyethylene (PE) assemblies, Phased Array Ultrasonic Testing (PAUT) is still studied extensively in several laboratories worldwide and is supported by the technical standard ISO TS 16943. During the last 10 years, several joint projects have been completed aiming at evaluating the acuity of PAUT applied to both pipes and electrofused assemblies either exhumed from the field or prepared in laboratory. More recently, a focus has been made on fixing some acceptance criteria combining PAUT data and long term resistance of the laboratory joints. This paper presents the updated data obtained on electrofused assemblies—63 mm saddles and 110 mm sockets—containing different types of defects such as: insufficient heating time, pipe under-penetration in the socket, excessive localized scraping, pollutants and calibrated thin strips, in both mass and cross configuration, put at the interface pipe-saddle. PAUT scanning on the different specimens, both during the welding phase and after cooling, confirms the capability of the technique to visualize and size the Heat Affected Zone (HAZ), which can be revealed and compared afterwards on sample sections. Moreover, most of the defects are detected and sized, confirming the fairly good performance of PAUT, except for the smallest strips which are located in non accessible zones, due to the particular design of the saddle. Long term resistance of the welds is then evaluated by Hydrostatic Pressure Tests (HPT) followed by a decohesion test after rupture, according to the requirements of both the ISO 13956 and NF EN 1555 standards. Under such test conditions, every joints comply with the requirements of the standards (rupture time greater than 1000 h at 80 °C and 5 MPa), even those violating the critical proportions of non-welded zones.

Carbon fiber reinforced plastics inevitably develop defects such as delamination, inclusions, and impacts during manufacturing and usage, which can adversely affect their performance. Ultrasonic phased array inspection is the most effective method for conducting nondestructive testing to ensure their quality. However, the diversity of defects within carbon fiber reinforced plastics makes it challenging for the current ultrasonic phased array inspection techniques to accurately identify these defects. Therefore, this paper presents a method for the ultrasonic phased array nondestructive testing and classification of various internal defects in carbon fiber reinforced plastics based on convolutional neural networks. We prepared an ultrasonic C-scan dataset containing multiple types of internal defects, analyzed the defect features in the ultrasonic C-scan images, and established an autoencoded classifier network to recognize manufacturing defects and impact defects of varying sizes. The experiments showed that the proposed method demonstrates superior defect feature extraction capabilities and can more accurately identify both impact and manufacturing defects.