This PDF file contains the front matter associated with SPIE Proceedings Volume 11594, including the Title Page, Copyright information, and Table of Contents.

Digital Transformation is easier said than done. The multi-disciplinary nature of the technology, and the multiple stakeholders in a successful application, calls for a system’s perspective; where in active contributors can appreciate and support each other’s roles. While the value proposition of NDE 4.0 as a system is clear – Safety and Economic value, the driving force at stakeholder levels is not same for all parties. All stakeholders need to appreciate and support each other with know-how, technology, and development roadmap to the extent possible. NDE 4.0 is team sport, no single player can win by doing their thing in isolation. This paper is aimed at sharing and inspiring an emerging sentiment for cooperation across the ecosystem so everyone wins bigger together.

With each revolution industry, maintenance, capital and consumer devices, infrastructure, society, and many more are growing closer with rising mutual influence. The third revolution started the digitization of data and the digitalization of processes, which are now omnipresent. The fourth revolution takes the next step by digital transformation, by bringing all assets from all different areas together. It requires data transparency and enables the Internet of Things, digital twins and threads, as well as smart cities. It presents the opportunity for everybody to use the data from technical and medical testing/imaging in conjunction with high repetition sensor information enabling prediction and prescription.

There is a higher necessity for a safe and intelligent railway transportation system as an important foundation for the smart city concept. The need to develop real-time condition monitoring technology for limited access parts of high-speed trains, such as wheels, is an important challenge. This paper develops an Internet of Things (IoT) based nondestructive evaluation (NDE 4.0) platform for autonomous inspection of in-service train wheels. The proposed NDE 4.0 platform consists of a wireless transmission module (WTM) which is used to remotely transfer power from the bogie to its surrounded wheels and also to receive back the data from sensors installed on the axle box of the wheels. The WTM’s circuits were designed and simulated in LTSpice software. This paper reveals the great potential of using cyber-physical systems to intelligently manage big data and autonomously control National Railway Networks (NRN).

This study aims in developing an advanced 3d digitization and nondestructive evaluation methodology that will serve as a baseline to advanced Augmented Reality (AR) and Virtual Reality (VR) applications of sites with archaeological interest in order to enhance not only the on-site experience for the visitors and the visitors via internet but as well to provide a useful tool for the preservatives, through improved supervision processes during maintenance. To this regard, a consolidated database is being created by the use of the best combination of state-of-the-art techniques of 3D digitization not only of the archaeological site itself but also of archaeological findings that at this time are presented in the museum. All the aforementioned data, created a database of the monument that aims to provide an augmented and virtual experience which is beyond the state of the art not only to the visitors but also to the preservative teams and it will help definitely towards the sustainability of the monument.

Metal-matrix composites with active components have been investigated as a way to functionalize metals. As opposed to surface-mounted approaches, smart materials embedded in metals can be effectively shielded against the environment while providing in-situ sensing, health monitoring, actuation, or energy harvesting functions. Typical manufacturing approaches can be problematic, however, in that they may physically damage the smart material or degrade its electromechanical properties. For instance, non-resin-based embedment procedures such as powder metallurgy involve isostatic compression and diffusion bonding, leading to high process temperatures and breakdown of the electromechanical properties of the active component to be embedded. This paper presents the development and characterization of an aluminum-matrix composite embedded with piezoelectric polyvinylidene fluoride (PVDF) sensors using ultrasonic additive manufacturing (UAM). UAM incorporates the principles of solid-state, ultrasonic metal welding and subtractive processes to fabricate metal-matrices with seamlessly embedded smart materials and without thermal loading. As implemented in this study, the UAM process uses as-received, commercial Al 6061 tape foilstock and TE Connectivity PVDF film. In order to increase the mechanical coupling between the sensor and the metal-matrix without the aid of adhesives, the PVDF sensor is embedded with an empirically optimized pre-compression defined by the tape foils welded above the sensor. The specimen is characterized by tensile (d31 mode), bending (d31 mode), and compression tests (d33 mode) to evaluate its functional performance. Within the investigated load range, the specimen exhibits open-circuit sensitivities of 4.6 mV/N under uniaxial tension and 9.7 mV/N under compressive impulse tests with better than 95% linearity and frequency bandwidth of several kilohertz. The technology presented in this study could be applied for load and tactile sensing, impact detection and localization, thermal measurements, energy harvesting, and non-destructive testing applications.

Vanadium carbide (V8C7) nanowires (NWs) axially grown on carbon cloths (CCs) as multi-ion acceptable cathode for both lithium (LIBs) and sodium-ion batteries (SIBs) were reported here. Using a facile hydrothermal method, we grew V2O3 NWs on CCs and subsequently reduced them to V8C7 by annealing with carbon sources under a H2/Ar atmosphere. In striking contrast to V2O5 NWs cathodes obtained by annealing under air atmosphere, the V8C7 NWs exhibit outstanding cycle stability during 500 cycles, and good rate capability for both LIB and SIB. V8C7 NWs as cathode active materials for LIBs exhibited 203.9 mAh g-1 in specific capacity at 0.1C after 500 cycles, representing 91.12% in cyclic retention and coulombic efficiency of 99.84%. As cathodes in SIBs, the V8C7 NWs delivered 176.34 mAh g-1 in specific capacity at 0.1C during 300 cycles. Due to its defect sites by removal of the oxygen framework in V2O3 NWs during the annealing step, and preservation of their unique 1D NW structure, the superior electrochemical performance is obtained. Moreover, the CCs utilized here as the current collector, provided a flexible 3D substrate that was able to mitigate the volume changes during cation intercalation/deintercalation, in conjunction with high electrical conductivity and chemical stability.

Embedded optical fiber sensors (OFS) are an emerging technology that can address real-time monitoring of wellbore integrity for carbon storage, oil/gas, and geothermal systems. Optical fiber sensors are capable of physical and chemical monitoring to observe the structural health of wellbores during operations. While embedded sensors add real-time monitoring capabilities, it is vital to understand how they interact with cement to impact the physical, mechanical, and flow properties of the cement in a well. Previous results showed that embedded OFS prototypes improved cement mechanical strengths and increased the axial permeability when OFS ran through the full length of the cement core. To simulate the most susceptible part of a cemented well, OFS prototypes were embedded within cement with a cement end cap at one end. The samples were then CT scanned for sample visualization and to determine the end cap thickness. Physical and mechanical properties (porosity, permeability, Young’s modulus, etc.) were measured on the sensor embedded cement samples. The cement cap demonstrated promising results in mitigating the undesired permeability increase for the OFS prototypes, while largely maintaining the mechanical enhancement.

The interest in observation of the dynamic behavior of bridges have been increasing in the recent years. The movement of bridge deck plays a significant role in the safety of bridges. In this project work, a direct and indirect sensor mounted on the bridge structure and on the passing vehicle are used for structural health monitoring. The overall study has been implemented based on six reliable approaches, including Gradient Boosting regression, Random Forest Regression, Ridge Regression, Support Vector Regression, Elastic Net Regression, XGBoost Regression and Support Vector Regression to get accurate results of prediction for structural health condition. For each of these regression models, the following performance evaluations are obtained: Mean Square Error (MSE), Root Mean Square Error (RMSE) and Rsquared. After obtaining all performance evaluations, the comparison of each of these metrics are done for all the six regressors. Finally, by using a Voting Regression, these six regression models are combined and used to train the entire dataset and predict on the test set. By using voting regression an ensemble model is proposed for this experiment.

Cable net structure is a typical tension structure, which can bear load by tension. The cable net structure has unique aesthetic feeling in the expression of architectural form, and has the advantages of light weight, easy folding and large shrinkage ratio, which also makes the cable net structure favored by designers and widely used in architectural structure. The roof of the natatorium of Suzhou Olympic Sports Center adopts 107m long-span saddle-shaped single-layer orthogonal cable-net structure. In this paper, the finite element analysis of the static performance of the cable-net steel structure roof was carried out. The overall calculation model of steel roof was established by using the finite element software. Considering the obvious geometric nonlinearity of single-layer orthogonal cable-net-membrane structure, the influence of static load, live load, wind load and snow load on the displacement and internal force of steel roof was analyzed under different load combinations.

Network intrusion detection systems (NIDS) for Internet-of-Things (IoT) infrastructure are among the most critical tools to ensure the protection and security of networks against malicious cyberattacks. This paper employs four machine learning algorithms and evaluates their performance in NIDS considering the accuracy, precision, recall, and F-score. The comparative analysis conducted using the CICIDS2017 dataset reveals that the Boosted machine learning techniques perform better than the other algorithms reaching the predicted accuracy of above 99% in detecting cyberattacks. Such ML-based attack detectors also have the largest weighted metrics of F1-score, precision, and recall. The results assist the network engineers in choosing the most effective machine learning-based NIDS to ensure network security for today’s growing IoT network traffic.

Current approaches to qualification or reliability assessment of NDE procedures by traditional probability of detection (POD) testing per MIL-HDBK-1823A require significant number of specimens with known size real or simulated flaws. For many practical applications in aerospace industry, either it is difficult to produce or it is economically not feasible to fabricate the necessary flaw specimens in required quantity. Thus, there is a strong need for alternative methodologies to provide assessments of reliability of NDE procedures using fewer flaw specimens. The paper provides an alternate NDE reliability assessment approach using dependence of the probability of detection (POD) and probability of false positive (POF), on the contrast-to-noise ratio (CNR) and decision threshold-to-noise ratio (TNR) for selected POD and POF models. The NDE reliability assessment approach is termed as limited sample (LS) POD assessment. Current work assumes a multi-hit flaw detection methodology i.e. the flaw detection calls are mapped to form an indication with cluster of pixels in a 2D image. In LS POD approach, the signal response from a fixed size flaw of interest is assumed to follow a normal distribution. The approach uses statistical tolerances computed using signal response sample data and k₁-factor. Similar to traditional POD analysis, a flaw size with chosen POD and confidence is determined. Alternately, POD/Conf. can be determined for a chosen decision threshold for the flaw size used in the analysis. The POF is also estimated in the analysis. Noise is assumed to follow either a normal or lognormal distribution. For reliable detection of the target size flaw, minimum POD/Conf. of 90/95% and maximum POF of 1% are assumed. The sample of signal responses is assumed to be representative of the assumed population of signal responses. Similarly, the noise measurements are assumed to be representative of those expected in the inspection of real hardware. If representative flaw sample and noise measurements are used, LS POD results pose no risk in the POD and POF estimation. Risk to meeting NDE flaw size detection requirements can be assessed based on how representative the flaw sample and noise measurements are and based on positive margin between LS POD results and procedure reliability requirements.