In this article, the theoretical and experimental aspects of three active thermography approaches: pulsed thermography (PT), lock-in thermography (LT), and vibrothermography (VT), are discussed in relation to the nondestructive evaluation (NDE) of honeycomb sandwich structures. For this purpose, two standard specimens with simulated defects (delaminations, core unbonds, excessive adhesive, and crushed core) were tested, and results were processed, examined, and compared. As will be pointed out, the similarities and differences between these active approaches allow conclusions to be made about the most suitable approach for a particular application. In addition, results from NDE inspection by X-rays and c-scan ultrasounds are provided and discussed for reference.

With the new industrial revolution of digital transformation, more intelligence and autonomous systems can be adopted in the manufacturing transportation processes. Safety and security of autonomous vehicles (AVs) have obvious advantages of reducing accidents and maintaining a cautious environment for drivers and pedestrians. Therefore, the transformation to data-driven vehicles is associated with the concept of digital twin, especially within the context of AV design. This also raises the need to adopt new safety designs to increase the resiliency and security of the whole AV system. To enable secure autonomous systems for smart manufacturing transportation in an end-to-end fashion, this article presents the main challenges and solutions considering safety and security functions. This article aims to identify a standard framework for vehicular digital twins that facilitate the data collection, data processing, and analytics phases. To demonstrate the effectiveness of the proposed approach, a case study for a vehicle follower model is analyzed when radar sensor measurements are manipulated in an attempt to cause a collision. Perceptive findings of this article can pave the way for future research aspects related to employing digital twins in the AV industry.

Using aerial platforms for Non-Destructive Inspection (NDI) of large and complex structures is a growing field of interest in various industries. Infrastructures such as: buildings, bridges, oil and gas, etc. refineries require regular and extensive inspections. The inspection reports are used to plan and perform required maintenance, ensuring their structural health and the safety of the workers. However, performing these inspections can be challenging due to the size of the facility, the lack of easy access, the health risks for the inspectors, or several other reasons, which has convinced companies to invest more in drones as an alternative solution to overcome these challenges. The autonomous nature of drones can assist companies in reducing inspection time and cost. Moreover, the employment of drones can lower the number of required personnel for inspection and can increase personnel safety. Finally, drones can provide a safe and reliable solution for inspecting hard-to-reach or hazardous areas. Despite the recent developments in drone-based NDI to reliably detect defects, several limitations and challenges still need to be addressed. In this paper, a brief review of the history of unmanned aerial vehicles, along with a comprehensive review of studies focused on UAV-based NDI of industrial and commercial facilities, are provided. Moreover, the benefits of using drones in inspections as an alternative to conventional methods are discussed, along with the challenges and open problems of employing drones in industrial inspections, are explored. Finally, some of our case studies conducted in different industrial fields in the field of Non-Destructive Inspection are presented.

In this paper, optical and mechanical excitation thermography was used to investigate basalt-fiber-reinforced polymer, carbon-fiber-reinforced polymer, and basalt-carbon fiber hybrid specimens subjected to impact loading. Interestingly, two different hybrid structures including sandwich-like and intercalated stacking sequence were used. Pulsed phase thermography, principal component thermography, and partial least-squares thermography (PLST) were used to process the thermographic data. X-ray computed tomography was used for validation. In addition, signal-to-noise ratio analysis was used as a means of quantitatively comparing the thermographic results. Of particular interest, the depth information linked to Loadings in PLST was estimated for the first time. Finally, a reference was provided for taking advantage of different hybrids in view of special industrial applications.

Prognostic and health management (PHM) plays a vital role in ensuring the safety and reliability of aircraft systems. The process entails the proactive surveillance and evaluation of the state and functional effectiveness of crucial subsystems. The principal aim of PHM is to predict the remaining useful life (RUL) of subsystems and proactively mitigate future breakdowns in order to minimize consequences. The achievement of this objective is helped by employing predictive modeling techniques and doing real-time data analysis. The incorporation of prognostic methodologies is of utmost importance in the execution of condition-based maintenance (CBM), a strategic approach that emphasizes the prioritization of repairing components that have experienced quantifiable damage. Multiple methodologies are employed to support the advancement of prognostics for aviation systems, encompassing physics-based modeling, data-driven techniques, and hybrid prognosis. These methodologies enable the prediction and mitigation of failures by identifying relevant health indicators. Despite the promising outcomes in the aviation sector pertaining to the implementation of PHM, there exists a deficiency in the research concerning the efficient integration of hybrid PHM applications. The primary aim of this paper is to provide a thorough analysis of the current state of research advancements in prognostics for aircraft systems, with a specific focus on prominent algorithms and their practical applications and challenges. The paper concludes by providing a detailed analysis of prospective directions for future research within the field.