This PDF file contains the front matter associated with SPIE Proceedings Volume 12489, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Digital transformation is the most misused term in recent years in addition to being a popular sales buzzword. Part of the confusion comes from it being a long journey with an ever-changing destination and new pathways emerging all the time. All of us can only see a small fraction of the vast landscape based on our past experiences and current context, and yet believe it to be the universe. Based on our worldview we fall into the trap of being right albeit incomplete. In this paper, authors take a holistic view of the industrial society and demystify frequently used terms - digitization, digitalization, and digital transformation. They discuss these three terms in the context of cyber-physical perception and show the interdependence with reliability assessments.

Floodlight Software hosts a podcast where Ms. Azari interviews various guests about important topics in NDE 4.0. In this presentation she will summarize parts of these conversations, share emerging themes, and analyze and draw some conclusions about the information provided by her guests. Podcast topics range from why NDE 4.0 exists and how it came to be, to considerations around technologies such as cloud-based computing and AI, to policy issues relating to safety and ethics and also to mindset challenges that risk slowing down adoption of NDE 4.0. This presentation is designed to be thought-provoking and to generate innovative ideas in our combined efforts to make NDE 4.0 a reality across the NDT industry. Audience participation is welcome! We hope that participants will share additional ideas and leave with a more comprehensive perspective of the opportunities and challenges associated with NDE 4.0.

The new crack luminescence method offers the possibility of making fatigue surface cracks in metallic materials more visible during inspections through a special coating system. This coating system consists of two layers, whereby the first layer has fluorescent properties and emits visible light as soon as it is irradiated by UV light. The top layer is black and is designed to prevent the fluorescent layer from emitting if no crack develops in the underlying material. The technique proved particularly useful in a wide variety of fatigue tests of steel components under laboratory conditions. Moreover, it has the potential to be used in various industrial applications. To enable industrial deployment and integration into maintenance strategies, a concept study is developed in this contribution, resulting in a qualification framework that can serve as a foundation for determining the reliability of the crack luminescence system in terms of a probability of detection curve. Within this study, factors causing measurement variability and uncertainty are being determined and their influences assessed. Due to the extension of the system by a moving computer vision system for automated crack detection using artificial intelligence, additional long-term effects associated with structural health monitoring systems need to be incorporated into an extended probability of detection study as part of the technical justification. Finally, important aspects and findings related to design of experiments are discussed, and a framework for reliability assessment of a new optical crack monitoring method is presented, emphasizing the influence of various uncertainty parameters, including long-term effects such as system ageing.

This paper presents the Airframe Digital Twin (ADT) framework and key technologies for aircraft structural life-cycle management, developed by the National Research Council (NRC) of Canada, with the aim of significantly reducing maintenance cost and extending the remaining useful life of aircraft components. The NRC ADT technologies include high-fidelity structural modelling, probabilistic usage/loads forecasting, probabilistic crack growth modelling, Bayesian updating based on non-destructive inspection (NDI) results, and advanced risk/reliability analysis. To demonstrate the NRC ADT framework, a CF-188 full-scale life-extension test was used as a physical platform to simulate the remaining lifespan of an aircraft component. A series of eddy-current NDI results, obtained during the CF-188 full-scale test, were processed using a Bayesian inference algorithm to update the ADT model. The updated ADT model was then used to predict the remaining service life of the component and to determine the next inspection interval based on the acceptable probability of failure defined by risk-based airworthiness management policies. The ADT-based methods and results were compared with the existing CF-188 lifing approach, which revealed advantages and gaps of the ADT framework for the future aircraft structural life-cycle management in the digital age. This work demonstrated the unique capability of the ADT framework to quantify the effect of NDI capability and reliability, which is crucial to update the ADT model and achieve its benefits for structural life assessment and maintenance scheduling.

Digital twin engineering is a disruptive technology that creates a living data model of industrial assets. The living model will continually adapt to changes in the environment or operations using real-time sensory data as well as forecast the future of the corresponding infrastructure. A digital twin can be used to proactively identify potential issues with its real physical counterpart, allowing the prediction of the remaining useful life of the physical twin by leveraging a combination of physics-based models and data-driven analytics. The digital twin ecosystem comprises sensor and measurement technologies, industrial Internet of Things, simulation and modeling, and machine learning. This paper will review the digital twin technology and highlight its application in predictive maintenance applications.

Electricity data sensors are widely used across large buildings and households. As the data is collected by distributed sensors from varied locations, privacy-preserving becomes a top concern for data owners. Meanwhile, multiple deep learning models achieved state-of-art performance on forecasting with the electricity time series data in a centralized training mechanism. Although these deep learning models are powerful at capturing temporal features and making precise predictions, it usually consumes a large amount of memory and resources during the training process. To address two problems, i.e., the data privacy issue and high-demanded resources for training, we propose an efficient and practical deep learning model using a transformer framework while utilizing federated learning to move the training on local data instead of on a centralized place. With the proposed deep learning model, the computation will reduce its memory usage by 60% while achieving similar and even better results on forecasting with the electricity time series data. Case studies on the university communities’ building demonstrate our proposed solution’s great potential and comparative performance compared to the state of the arts.

Within quality critical industries, e.g. aerospace, quality control with non-destructive evaluation (NDE) is essential. The surface quality is often important and e.g. visual inspection is often applied. Part of the inspection is the data interpretation, not easily made automatic for critical products. Recent studies on the automatization have indicated promising results utilizing deep-learning-based artificial intelligence. However, many such algorithms are known to be overconfident when subjected to unexpected input (e.g. new/rare material defects) far from the training dataset, so-called out-of-distribution (OOD) data. We claim that safe computer-based interpretation of NDE data within quality critical applications, must respond sensible also to OOD data. A sensible response could be that the algorithms identify such OOD data and forward it to a human for further analysis. Such an OOD detector could facilitate a human-machine collaboration in a NDE 4.0 vision. In this work we have explored if a recently proposed (for industrial x-ray images) auto-encoder-based approach can be utilized as OOD detector (one-class classifier) for visual inspection data. The model is trained in an unsupervised manner on accepted input to reconstruct it at high precision. Simultaneously it is trained to remove synthetically added defect indications to generate a clean image patch, similar to denoising-auto-enoders. The difference between the input and reconstructed input is analyzed for OOD detection. We train and test the algorithm on a publicly available visual inspection dataset with surface defects. We achieve true positive rates at 0.90 with true negative rates at 0.99 and demonstrate detection of OOD data.

Cracks on sheet metals can significantly affect the overall strength. Crack detection during manufacturing is, thus, an important process for the quality assessment on a press line. Deep learning, a data-driven structure, has been extensively used to detect cracks on various surfaces. In this study, a crack detection technique for a press line using Retina Net and a novel data augmentation method is proposed, which mainly focuses on three steps, shape acquisition, style transfer, and edge fusion. First, the shapes of crack on different materials are extracted. Then, images are created by providing metal crack textures to those shapes using a fusion network with a relatively small number of real crack images. Real crack images are captured from a sheet metal forming line. Training data can be enriched using the proposed data augmentation method. Validation experiments are conducted to demonstrate the effectiveness of the proposed crack detection and data augmentation techniques.

Industry 4.0 is based on digitization, information crosslinking and networks. In this investigation an Industrial Internet of Things (IIoT) architecture developed by the authors is used in conjunction with NDE datasets for near real-time diagnostics of crack initiation. Acoustic Emission datasets were acquired using aerospace-grade aluminum alloy and were subsequently used in the IIoT system, which is capable of Edge, Fog, and Cloud computing. The main innovation of this approach is a combination of hardware, computing and Machine Learning analysis proves to be advantageous in implementing a data structure that can successfully flag the incubation and subsequent initiation of fracture.

In recent years, real-time monitoring of health conditions for massive structures, such as bridges and buildings, has grown in interest. Some of the key factors are the possibility to estimate continuously the health condition, as well as a reduction in the personnel involved in visual inspections and operative costs. However, while dealing with such structures, it is extremely rare to observe anomaly conditions, and when those are met is in general too late. Consequently, the structural health monitoring problem must be tackled as an unsupervised one. The idea exploited in this research is to transform the intrinsically unsupervised problem into a supervised one. Considering a structure equipped with N sensors, which measure static or quasi-static quantities (distance, inclinations, temperatures, etc.), it could be helpful to evaluate if the relations among sensors change over time. This involves the training of N models, each of them able to estimate the quantity measured by a sensor, by using the other N-1 measurements. In this way, an ensemble of models representing the system is built (iterative model). This approach allows us to compare the expected measurement of every sensor with the real one. The difference between the two can be addressed as a symptom of modifications in the structure with respect to the nominal condition. This approach is tested on a real case, i.e. the Candia bridge in Italy.

The estimation of trains weight could be useful under certain circumstances. For instance, in the field of structural health monitoring, some considerations can be derived from the evaluation of the load spectrum that an infrastructure has to withstand in its lifetime. One approach to estimate the train weight is based on the use of strain gauges mounted on the rail. The procedure allows to associate the local deformations with the load on an axle. However, strain gauges present several limitations: they are regarded as delicate sensors, and their replacement is burdensome and time-consuming. Moreover, their life is usually short when subjected to weathering and numerous load cycles. For these reasons, this paper proposes a novel methodology that relies on the use of more robust sensors mounted on a bridge structure for the estimation of the train load, alongside other information, such as the number of axles, the train speed, and the train class. The idea consists in the estimation of the train load starting from a network of sensors mounted on a bridge. A deep learning model is particularly suitable to achieve this task. The sensors network must consist of robust and easy-to-replace transducers (such as velocimeters mounted on the bridge structure). In this way, when the strain gauges are removed, the system is still able to estimate the loads passing on the bridge.

This paper investigates the characterization and functional performance of a piezoelectric polyvinylidene fluoride (PVDF) sensor embedded into an aluminum plate using ultrasonic additive manufacturing (UAM). While conventional manufacturing techniques such as non-resin-based powder metallurgy are being used to surface-mount smart materials to metals, they pose their own set of problems. Standard manufacturing approaches can physically damage the sensor or deteriorate electrochemical properties of the active material due to high processing temperatures or long adhesive settling times. In contrast, UAM integrates solid-state metal joining with subtractive processes to enable the fabrication of smart structures by embedding sensors, actuators, and electronics in metal-matrices without thermal loading. In this paper, a commercial PVDF sensor is embedded in aluminum with a pre-compression to provide frictional coupling between the sensor and the metal-matrix, thus eliminating the need for adhesives. Axial impact and bending (shaker) tests are conducted on the specimen to characterize the PVDF sensor’s frequency bandwidth and impact detection performance. Metal-matrices with active components have been under investigation to functionalize metals for various applications including aerospace, automotive, and biomedical. UAM embedment of sensors in metals enables functionalization of structures for measurement of stresses and temperature within the structure while also serving to shield smart components from environmental hazards. This technique can serve a wide-range of applications including robotics and tactile sensing, energy harvesting, and structural health monitoring.

Data collected in active infrared thermography (AIRT) experiments for non-destructive defect detection in materials are often contaminated by undesired noise and backgrounds. In this study, an AIRT data processing method, which adopts adaptive fixed-rank kriging, is proposed. This approach computes a set of ordered functions that represent data features at the different resolution levels, called multi-resolution spline basis functions. Multiresolution spline functions were extracted from the thin-plate splines and ordered by the degree of smoothness. The only tuning parameter for this method is the resolution level, making this approach extensively applicable. The performance of the proposed method was evaluated by conducting a mosaic sample defect detection. The results showed that the proposed AIRT data processing method is not only efficient but also effective.

In the field of structural health monitoring, the adoption of intelligent systems able to automatically detect changes in a structure are evidently attractive. A change in the baseline configuration can be an early predictor of a structural defect that has to be monitored before it reaches critical conditions. When there is no prior knowledge on the system, deep learning models such as autoencoders could effectively detect a change and enhance the capability to determine the damage location. In this paper a deep learning approach is applied to a test rig consisting of a small building model composed by four floors connected by bending springs. Modifications of the system are simulated by changing stiffness of the spring. This algorithm is compared with traditional approach based on modal parameters by carrying out experimental tests to validate the hypothesis.

Since 2019 researchers in the field of deep learning have been exploring the possibilities of Physics Informed Neural Networks (PINN). The training of regular neural networks (NNs) involved an optimization where the loss function depends exclusively on the dataset available. In PINN this loss function takes into account also the physics of the problem, if it is known and the governing equations are given. This paper explores the advantages of the use of PINNs with respect to regular NNs, in the privileged case where a multibody model is available. However, there is still uncertainty around how much weight should be associated with each of the two losses (data-driven loss and physics loss). Therefore, different weights for the two losses are considered and their effect on the performance of the model is evaluated. The research focuses on the synthesis of a four-bar mechanism for trajectory planning of a point belonging to the connecting rod. The objective is to generate a tool that synthesizes the mechanism topology given the desired trajectory. This preliminary study shows how PINN are suitable to automatize the synthesis of mechanisms, where regular NN would generally fail. Numerical analyses also demonstrate that a PINN learns relations from a physical numerical model in a more efficient way than a traditional NN.

The effects of the lightning strike on composite aircraft structures have been an active research area in the aviation industry, given the concern over safe aircraft operations. To maintain safe operations, civil and military regulators require effective approaches to assess and quantify the severity of lightning damage. Although x-rays are commonly used to determine material damage in aircraft structures, the technique requires access to both sides of the investigated part. This paper proposes a novel autoencoder model to check the feasibility of evaluating the damage to carbon fiber reinforced polymers (CFRP) panels from the outer surface of in-service aircraft structures. Two alternative techniques to x-ray, such as ultrasonic testing (UT) and infrared thermography (IR), nondestructive evaluation methods, are employed to develop the proposed model. The fusion model uses U-net as the backbone and spatial attention fusion as the fusion strategy while combining structural similarity index (SSIM) and perceptual losses as the loss function. Also, the log-Gabor filter is used in the model to obtain high-frequency edge information for fusion. The results are then compared against five state-of-the-art fusion methods, revealing that the proposed model performs better in quantifying the lightning damage to aircraft CFRP structures.

In additive manufacturing, laser powder bed fusion (LPBF) has unrivaled strengths due to its design and manufacturing freedom. The in situ validation of additively manufactured components would reduce or entirely remove the need for post-processed non-destructive evaluation. Potentially enabling the direct utilization of components from the print bed. However, typical approaches to in situ monitoring of the LPBF process utilize high-speed thermal and optical cameras coupled with advanced optics to enable co-axial imaging of the weld pool. The amount and quality of the data obtained through these systems necessitate the need for extensive post-processing of data. In contrast, this work provides a low-cost in situ monitoring and real-time computing alternative using industrial cameras and optical filters to track the splatter area of the welding process. To reduce the dimensionality of data retained for a given component, the proposed process tracks the brightness contours of the welding process in real-time and retains only a select number of features. In this introductory work, the prototype system is investigated using a variety of different image processing methods to optimize processing speed (measured in frames per second) versus the size of melting splatter for a test specimen of 10 mm × 10 mm × 5 mm. Defects in the specimen are quantified using computed tomography and linked to information extracted from tracking the splatter-related features in situ. Results show that the speed of the computational system, visibility of splatter, and the accurate translation of splatter brightness to contours with area and locations is critical to functionality. A discussion on the trade-offs between these constraints is provided.