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This paper introduces Buckingham's theorem in the context of Non-Destructive Testing & Evaluation (NDT&E). Its application leads to easier problems to handle by reducing the number of variables involved. In this sense, dimensional analysis can provide the foundation for in-line, real-time and low-cost inspection methods that are fully compatible with the requirements of the Industry 4.0 and NDE 4.0 paradigms. In order to show the impact of the Buckingham's theorem in NDT&E, we consider a practical case of interest, i.e. the simultaneous estimation of thickness and electrical conductivity of metallic plates via Eddy Current Testing. An initial numerical analysis is carried out with the aim to show the metrological performance of the method. The results obtained show that the method combines good accuracy with low computational costs. [ABSTRACT FROM AUTHOR]

In this work, we present a system architecture that is especially designed for remote ultrasound testing inspections (UT). The system itself is realized using a real-time session service and a web service based on a REST API (REST: Representational State Transfer; API: application programming interface). This web service is used to store data persistently, e.g., sample geometries, raw nondestructive testing (NDT) data and derived inspection results in a shared dataspace. In the current development state, the results consist of textures mapped onto the sample geometry. This approach allows us to display the UT results directly on the real sample using mixed reality technologies. We also implemented a feature to assist the inspector remotely by making use of the availability of this digital representation. Hence, it is necessary to share additional data like the current UT-signal, temporary position marks, user and device positions, etc. between the different participants. A real-time distribution of this highly dynamic data is required to create an effective assistance environment. Therefore, a separate session service is used. The inspection data generated in this temporary session can also be transferred to the afore mentioned dataspace to be saved persistently. The system features mixed reality visualization for the inspector and optionally a virtual reality or a 3D Desktop environment for one or more remote assistants. [ABSTRACT FROM AUTHOR]

Air-coupled ultrasonic (ACU) testing has been used for several years to detect defects in plate-like structures. Especially, for automated testing procedures, ACU testing is advantageous in comparison to conventional testing. However, the evaluation of the measurement data is usually done in a manual manner, which is an obstruction to the application of ACU testing. The goal of this study is to automate and improve defect characterization and NDE 4.0 accordingly with deep learning. In conventional ACU testing the measurement data contains temporal (A-scans) and spatial (C-scans) information. Both data types are investigated in this study. For the A-scans, which represent time series data, neural network architectures tailored to such data types are applied. In addition, it is evaluated if further adaptions of the training procedure increase the performance. The C-scans are segmented by applying different U-net similar architectures and training strategies. In order to use spatial and temporal information, a further approach is taken. The prediction of the time series models is segmented with image models. The performance of all trained models and training strategies is compared with the F1-score and benchmarked against the conventional evaluation, which is thresholding of the C-scans. As specimens, artificial defects in acrylic and carbon fiber-reinforced polymer plates are investigated. [ABSTRACT FROM AUTHOR]

Knowledge about the location of a defect is essential for damage assessment. In terms of a digitalised maintenance, inspection data is combined with position information. The presented approach regards the manual ultrasonic inspection, where the ultrasonic probe and the inspected component are both hand-held. By using markerless tracking technologies, it is possible to track the component without any markers. The ultrasonic probe is tracked by a more stable marker-based tracking technology. This results in a hybrid tracking system, which allows a referencing of the non-destructive testing (NDT) data directly to the local coordinate system of the 3D model that corresponds to the inspected component. Transferring this approach to other manual inspection technologies allows for a superimposition of recorded NDT data without any postprocessing or transformation. A better damage assessment is thus enabled. The inspection system, the inspection tool calibration and the camera registration process are described and analysed in detail. This work is focused on the analysis of the system accuracy, which is realised by using a reference body. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is a method of spatially mapping the conductivity distribution of a domain and has been studied as a potential embedded sensing or nondestructive evaluation (NDE) tool. An often touted advantage of EIT is that it can be used in-situ; that is, because the method only requires the application of unobtrusive electrodes, it can conceivably be used while the component or structure is in operation. This material-as-the-sensor philosophy strongly aligns with key components of the NDE 4.0 vision such as the realization of intelligent cyber–physical systems (CPS) and digital twins. To date, however, the claim of in-situ sensing via EIT has not been significantly substantiated. This is problematic because operational loads induce strains that often change the conductivity of the material. Establishing that EIT can detect damage-induced conductivity changes through the presence of unrelated strain-induced conductivity changes is therefore important. To that end, we herein study the application of EIT for detecting indentation damage in a carbon fiber/epoxy composite as the composite is loaded in a four-point bend. It was found that the bending load changes the contact impedance of the electrodes, which resulted in poor EIT images when solving the EIT inverse problem with the ℓ 2 -norm on the error term. Using the ℓ 1 -norm on the error term, solved via the primal–dual interior point method (PDIPM), significantly improved image quality. Image quality was even further improved through the use of a mixed prior for regularization, and EIT images were compared to thermography with good agreement. These results show that EIT can indeed detect damage through the presence of an applied load, but care must be taken to account for factors such as outlier data arising from electrode degradation and changing contact impedance. Use of the ℓ 1 -norm on the error term is therefore highly recommended for in-situ imaging via EIT. [ABSTRACT FROM AUTHOR]

Lightning strikes pose a significant challenge for aircraft and wind turbine blades with Carbon Fiber Reinforced Polymer (CFRP) structures, requiring reliable damage detection techniques. Non-destructive evaluation (NDE) methods, including X-ray and Ultrasonic Testing, are effective in identifying material damage in aircraft. However, X-ray requires access to both sides of the structure, and UT requires a coupling medium between the transducer and the structure, as well as a relatively smooth surface, making both methods less feasible for routine aircraft maintenance. Other NDE techniques, such as eddy current testing and infrared thermography, can detect damage on the side struck by lightning but lack the precision needed for a comprehensive assessment. To address these challenges, this paper introduces a two-stage Fusion-Translation network (FTnet), which integrates NDE 4.0 innovations, including data fusion and advanced imaging algorithms, to optimize the NDE process. By integrating infrared and eddy current data, FTnet characterizes lightning-induced damage with enhanced depth and contour detail, demonstrating superior performance over existing methods in both qualitative and quantitative evaluations. The implementation of FTnet marks an advancement in NDE 4.0, potentially enhancing aircraft safety and streamline maintenance protocols by providing a more reliable and comprehensive assessment of lightning strike damage. [ABSTRACT FROM AUTHOR]

Nondestructive evaluation (NDE) techniques are used in many industries to evaluate the properties of components and inspect for flaws and anomalies in structures without altering the part's integrity or causing damage to the component being tested. This includes monitoring materials' condition (Material State Awareness (MSA)) and health of structures (Structural Health Monitoring (SHM)). NDE techniques are highly valuable tools to help prevent potential losses and hazards arising from the failure of a component while saving time and cost by not compromising its future usage. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML) techniques are useful tools which can help automating data collection and analyses, providing new insights, and potentially improving detection performance in a quick and low effort manner with great cost savings. This paper presents a survey on state of the art AI-ML techniques for NDE and the application of related smart technologies including Machine Vision (MV) and Digital Twins in NDE. [ABSTRACT FROM AUTHOR]

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In this paper we present an approach where ultrasonic testing data (UT) is linked with its spatial coordinates and direction vector to the examined specimen. Doing so, the processed nondestructive testing (NDT) results can be visualized directly on the sample in real-time using augmented or virtual reality. To enable the link between NDT data and physical object, a 3D-tracking system is used. Spatial coordinates and NDT sensor data are stored together. For visualization, texture mapping was applied on a 3D model. The testing process consists of data recording, processing and visualization. All three steps are performed in real-time. The data is recorded by an UT-USB interface, processed on a PC workstation and displayed using a Mixed-Reality-system (MR). Our system allows real-time 3D visualization of ultrasonic NDT data, which is directly drawn into the virtual representation. Therefore, the possibility arises to assist the operator during the manual testing process. This new approach results in a much more intuitive testing process and a data set optimally prepared to be saved in a digital twin environment. The size of the samples is not limited to a laboratory scale, but also works for larger objects, e.g. a helicopter fuselage. Our approach is inspired by concepts of NDE 4.0 to create a new kind of smart inspection systems. [ABSTRACT FROM AUTHOR]

Reliability evaluations of modern test systems under the Industry 4.0 technologies, play a vital role in the successful transformation to NDE 4.0. This is due to the fact that NDE 4.0 is mainly based on the interconnection between the cyber-physical systems. When the individual reliability of the various important technologies from the Industry 4.0 such as the digital twin, digital thread, Industrial Internet of Things (IIoT), artificial intelligence (AI), data fusion, digitization, etc. is high, then it is possible to obtain the reliability beyond the intrinsic capability of the test system. In this paper, the significance of the reliability evaluation is reviewed under the vision of NDE 4.0, including examples of data fusion concepts as well as the importance of algorithms (like explainable artificial intelligence), the practical use is discussed and elaborated accordingly. [ABSTRACT FROM AUTHOR]

Stage-gate process is a project management technique that has been used to manage major technology development and deployment projects for over 50 years. It emphasizes quality, risk, and value, and provides an orderly framework for decision making with multiple projects carried out concurrently. This paper will examine the potential application of this process to NDE 4.0 developments. This paper will also explore the factors that make a stage-gate process successful, and how biases affect the outcomes of gate reviews. It explores which biases are problematic and how we can de-bias our own interpretation of them through conscious awareness and re-thinking the Stage-Gate Process. It also discusses how to create an environment where biases are minimized or eliminated. During research and reporting of this manuscript, the human authors synthesized their decades of experience in engaging with technology development decisions, with contributions from an artificial intelligence agent GPT-3 on human biases and motivations. This was done to capture the firsthand experience of collaborating with AI in the spirit of increasing the role of machine intelligence in human–machine systems, typical of NDE. Since this appeared to be an innovative approach, the authors decided to use the gated process in creating this paper with AI as a co-author. The details are presented as a case study within the paper. The topic is important because, managers are struggling to embrace the NDE 4.0 initiative, despite its importance and their willingness. Leveraging AI partnership to even address this important topic is a testament to cognitive capability of learning machines to help inspection systems with creating test procedures, making decisions, and creating test reports. This manuscript is the first attempt at qualitative treatment of human factors in gated innovation process with research assisted by an Artificial Intelligence based language model (non-human). The first paragraph of the abstract above is completely AI generated. [ABSTRACT FROM AUTHOR]

Across so many industries, non-destructive evaluation has proven its worth time and again through quality and safety assurance of valuable assets. Yet, over time, it became underappreciated in business decisions. In most cases, the data gathered by NDT is used for quality assurance assessments resulting in binary decisions. And we seem to miss out on value of the information content of NDE which goes way deeper and can help other stakeholders: such as engineering, management, inspectors, service providers, and even regulators. Some of those groups might not even be aware of the benefits of NDE data and its digitalization. Unfortunately, the NDE industry typically makes the data access unnecessarily difficult by proprietary interfaces and data formats. Both those challenges need to be addressed now by the NDE industry. The confluence of NDE and Industry 4.0, dubbed as NDE 4.0, provides a unique opportunity for the NDE/NDT Industry to not only readjust the value perception but to gain new customer groups through a broad set of value creation activities across the ecosystem. The integration of NDE into the Cyber-Physical Loop (including IIoT and Digital Twin) is the chance for the NDE industry to now shift the perception from a cost center to a value center. This paper provides an overview of the NDE ecosystem, key value streams, cyber-physical loops that create value, and a number of use cases for various stakeholders in the ecosystem. [ABSTRACT FROM AUTHOR]

Like with the previous revolutions the goal of the fourth revolution is to make manufacturing, design, logistics, maintenance, and other related fields faster, more efficient, and more customer centric. This holds for classical industries, for civil engineering, and for NDE and goes along with new business opportunities and models. Core components to enable those fourth revolutions are semantic interoperability, converting data into information, the Industrial Internet of Things (IIoT) offering the possibility for every device, asset, or thing to communicate with each other using standard open interfaces, and the digital twin converting all the available information into knowledge and closing the cyber-physical loop. For NDE this concept can be used #1 to design, improve, and tailor the inspection system hard- and software and #2 to choose and adapt to best inspection solution for the customer or to enhance the inspection performance. Enabling better quality, speed, and cost at the same time. On a broader view, the integration of NDE into IIoT and Digital Twin is the chance for the NDE industry for the overdue change from a cost center to a value center. In most cases, data gathered by NDE is used for a quality assurance assessment resulting in a binary decision. But the information content of NDE goes way deeper and is of major interest for additional groups: engineering and management. Some of those groups might currently not be aware of the benefits of NDE data and the NDE industry makes the access unnecessarily difficult by proprietary interfaces and data formats. Both those challenges need to be taken on now by the NDE industry. The big IT players are not waiting and, if not available on the market, they will develop and offer additional data sources including ultrasonics, X-ray or eddy current. [ABSTRACT FROM AUTHOR]

Nondestructive evaluation (NDE) techniques are used in many industries to evaluate the properties of components and inspect for flaws and anomalies in structures without altering the part’s integrity or causing damage to the component being tested. This includes monitoring materials’ condition (Material State Awareness (MSA)) and health of structures (Structural Health Monitoring (SHM)). NDE techniques are highly valuable tools to help prevent potential losses and hazards arising from the failure of a component while saving time and cost by not compromising its future usage. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML) techniques are useful tools which can help automating data collection and analyses, providing new insights, and potentially improving detection performance in a quick and low effort manner with great cost savings. This paper presents a survey on state of the art AI-ML techniques for NDE and the application of related smart technologies including Machine Vision (MV) and Digital Twins in NDE.

Cyber technologies are offering new horizons for quality control in manufacturing and safety assurance in-service of physical assets. The line between non-destructive evaluation (NDE) and Industry 4.0 is getting blurred since both are sensory data-driven domains. This multidisciplinary approach has led to the emergence of a new capability: NDE 4.0. The NDT community is coming together once again to define the purpose, chart the process, and address the adoption of emerging technologies. In this paper, the authors have taken a design thinking approach to spotlight proper objectives for research on this subject. It begins with qualitative research on twenty different perceptions of stakeholders and misconceptions around the current state of NDE. The interpretation is used to define ten value propositions or use cases under ‘NDE for Industry 4.0’ and ‘Industry 4.0 for NDE’ leading up to the clarity of purpose for NDE 4.0—enhanced safety and economic value for stakeholders. To pursue this worthy cause, the paper delves into some of the top adoption challenges, and proposes a journey of managed innovation, conscious skills development, and a new form of leadership required to succeed in the cyber-physical world. [ABSTRACT FROM AUTHOR]

The challenges presented by the global pandemic and slump in oil prices have imposed costly avoidance measures and delayed project timeliness, but it also has created the opportunity for innovation conditions in industrial non-destructive testing. The evolutional path leveraged by Industry 4.0, present sustainable offerings of robotic platforms, digital solutions and connected devices commonly known as the Internet of Things (IoT) that may assist in recapturing some of the current losses. The landscape is broad with staggered adoptions. An overview of the Industrial Revolutions, related developments in NDE 4.0 and specific focus on North American radiography in the petroleum industry is highlighted. Additionally, focusing on the importance of shared transparency and burden throughout the value-chain to ensure efficacy throughout the migration, and the human contributor for collaborative transition and skills required for the future. The evolutional path of Industrial revolutions leads to Industry 4.0 which presents opportunities with Artificial Intelligence, and connected devices commonly known as the Internet of Things (IoT). This article focuses on three major issues for consideration in the development of a strategic plan to capitalize on the advancements of digital radiography in the petroleum sector. The components are the technological NDE 4.0 transformation, the need for a new and realistic perspective of digital radiography, and the challenges of developing a workforce to adapt to the transition of NDE 4.0. [ABSTRACT FROM AUTHOR]

Non-destructive evaluation of aircraft production is optimised and digitalised with Industry 4.0. The aircraft structures produced using fibre metal laminate are traditionally inspected using water-coupled ultrasound scans and manually evaluated. This article proposes Machine Learning models to examine the defects in ultrasonic scans of A380 aircraft components. The proposed approach includes embedded image feature extraction methods and classifiers to learn defects in the scan images. The proposed algorithm is evaluated by benchmarking embedded classifiers and further promoted to research with an industry-based certification process. The HoG-Linear SVM classifier has outperformed SURF-Decision Fine Tree in detecting potential defects. The certification process uses the Probability of Detection function, substantiating that the HoG-Linear SVM classifier detects minor defects. The experimental trials prove that the proposed method will be helpful to examiners in the quality control and assurance of aircraft production, thus leading to significant contributions to non-destructive evaluation 4.0. [ABSTRACT FROM AUTHOR]