A Review of Corrosion in Aircraft Structures and Graphene-Based Sensors for Advanced Corrosion Monitoring

Cellular Automata Modeling as a Tool in Corrosion Management

Cellular automata models have emerged as a valuable tool in corrosion management. This manuscript provides an overview of the application of cellular automata models in corrosion research, highlighting their benefits and contributions to understanding the complex nature of corrosion processes. Cellular automata models offer a computational approach to simulating corrosion behavior at the microscale, capturing the intricate interactions between electrochemical reactions, material properties, and environmental factors and generating a new vision of predictive maintenance. It reviews the key features of cellular automata, such as the grid-based representation of the material surface, the definition of state variables, and the rules governing cell-state transitions. The ability to model local interactions and emergent global behavior makes cellular automata particularly suitable for simulating corrosion processes. Finally, cellular automata models offer a powerful and versatile approach to studying corrosion processes, expanding models that can continue to enhance our understanding of corrosion and contribute to the development of effective corrosion prevention and control strategies.

Automated Identification of Hidden Corrosion Based on the D-Sight Technique: A Case Study on a Military Helicopter

Hidden corrosion remains a significant problem during aircraft service, primarily because of difficulties in its detection and assessment. The non-destructive D-Sight testing technique is characterized by high sensitivity to this type of damage and is an effective sensing tool for qualitative assessments of hidden corrosion in aircraft structures used by numerous ground service entities. In this paper, the authors demonstrated a new approach to the automatic quantification of hidden corrosion based on image processing D-Sight images during periodic inspections. The performance of the developed processing algorithm was demonstrated based on the results of the inspection of a Mi family military helicopter. The nondimensional quantitative measurement introduced in this study confirmed the effectiveness of this evaluation of corrosion progression, which was in agreement with the results of qualitative analysis of D-Sight images made by inspectors. This allows for the automation of the inspection process and supports inspectors in evaluating the extent and progression of hidden corrosion.

Physics-Informed, Data-Driven Model for Atmospheric Corrosion of Carbon Steel Using Bayesian Network

Atmospheric corrosion is a significant challenge faced by the aviation industry as it considerably affects the structural integrity of an aircraft operated for long periods. Therefore, an appropriate corrosion deterioration model is required to predict corrosion problems. However, practical application of the deterioration model is challenging owing to the limited data available for the parameter estimation. Thus, a high uncertainty in prediction is unavoidable. To address these challenges, a method of integrating a physics-based model and the monitoring data on a Bayesian network (BN) is presented herein. Atmospheric corrosion is modeled using the simulation method, and a BN is constructed using GeNie. Moreover, model calibration is performed using the monitoring data collected from aircraft parking areas. The calibration approach is an improvement over existing models as it incorporates actual environmental data, making it more accurate and applicable to real-world scenarios. In conclusion, our research emphasizes the importance of precise corrosion models for predicting and managing atmospheric corrosion on carbon steel. The study results open new avenues for future research, such as the incorporation of additional data sources to further improve the accuracy of corrosion models.

Multifunctional Integration of Optical Fibers and Nanomaterials for Aircraft Systems

Smart sensing for aeronautical applications is a multidisciplinary process that involves the development of various sensor elements and advancements in the nanomaterials field. The expansion of research has fueled the development of commercial and military aircrafts in the aeronautical field. Optical technology is one of the supporting pillars for this, as well as the fact that the unique high-tech qualities of aircrafts align with sustainability criteria. In this study, a multidisciplinary investigation of airplane monitoring systems employing optical technologies based on optical fiber and nanomaterials that are incorporated into essential systems is presented. This manuscript reports the multifunctional integration of optical fibers and nanomaterials for aircraft sector discussing topics, such as airframe monitoring, flight environment sensing (from temperature and humidity to pressure sensing), sensors for navigation (such as gyroscopes and displacement or position sensors), pilot vital health monitoring, and novel nanomaterials for aerospace applications. The primary objective of this review is to provide researchers with direction and motivation to design and fabricate the future of the aeronautical industry, based on the actual state of the art of such vital technology, thereby aiding their future research.

Monitoring of Hidden Corrosion Growth in Aircraft Structures Based on D-Sight Inspections and Image Processing

Hidden corrosion in aircraft structures, not detected on time, can have a significant influence on aircraft structural integrity and lead to catastrophic consequences. According to the widely accepted damage tolerance philosophy, non-destructive inspections are performed to assess structural safety and reliability. One of the inspection techniques used for such an inspection is the optical D-Sight technique. Since D-Sight is used primarily as a qualitative method, it is difficult to assess the evolution of a structural condition simply by comparing the inspection results. In the following study, the method to monitor hidden corrosion growth is proposed on the basis of historical data from D-Sight inspections. The method is based on geometric transforms and segmentation techniques to remove the influence of measurement conditions, such as the angle of observation or illumination, and to compare corroded regions for a sequence of D-Sight images acquired during historical inspections. The analysis of the proposed method was performed on the sequences of D-Sight images acquired from inspections of Polish military aircraft in the period from 2002 to 2017. The proposed method represents an effective tool for monitoring hidden corrosion growth in metallic aircraft structures based on a sequence of D-Sight images.

A Hybrid Microfluidic Electronic Sensing Platform for Life Science Applications

This paper presents a novel hybrid microfluidic electronic sensing platform, featuring an electronic sensor incorporated with a microfluidic structure for life science applications. This sensor with a large sensing area of 0.7 mm² is implemented through a foundry process called Open-Gate Junction FET (OG-JFET). The proposed OG-JFET sensor with a back gate enables the charge by directly introducing the biological and chemical samples on the top of the device. This paper puts forward the design and implementation of a PDMS microfluidic structure integrated with an OG-JFET chip to direct the samples toward the sensing site. At the same time, the sensor’s gain is controlled with a back gate electrical voltage. Herein, we demonstrate and discuss the functionality and applicability of the proposed sensing platform using a chemical solution with different pH values. Additionally, we introduce a mathematical model to describe the charge sensitivity of the OG-JFET sensor. Based on the results, the maximum value of transconductance gain of the sensor is ~1 mA/V at Vgs = 0, which is decreased to ~0.42 mA/V at Vgs = 1, all in Vds = 5. Furthermore, the variation of the back-gate voltage from 1.0 V to 0.0 V increases the sensitivity from ~40 mV/pH to ~55 mV/pH. As per the experimental and simulation results and discussions in this paper, the proposed hybrid microfluidic OG-JFET sensor is a reliable and high-precision measurement platform for various life science and industrial applications.

Hydrogenated Graphene Based Organic Thin Film Transistor Sensor for Detection of Chloride Ions as Corrosion Precursors

Corrosion monitoring and management has been at the center of structural health monitoring protocols due to its damaging effects on metallic structures. Current corrosion prevention and management programs often fail to include environmental factors such as Cl− ions and surface wetness. Early detection of these environmental factors can prevent the onset of corrosion and reduce repair and maintenance-related expenses. There is growing interest in creating solution-processed thin film environmental sensors with high sensitivity to corrosion precursors, low-cost fabrication, and small footprint, rendering them viable candidates for investigation as potential corrosion sensors that could be easily integrated into existing structures and screen printed or patterned directly into surface coatings. In this work, we have implemented C60-based n-type organic thin film transistors (OTFTs) with functionalized graphene oxide for humidity sensing and functionalized graphene nanoparticles for Cl− ion detection, using low-cost solution processing techniques. The reduced graphene oxide (rGO)-coated OTFT humidity sensor is designed for the qualitative estimation of surface moisture levels and high levels of humidity, and it exhibits a relative responsivity for dry to surface wetness transition of 122.6% to surface wetness, within a response time of 20 ms. We furthermore implemented an in-house synthesized hydrogenated graphene coating in conjunction with a second OTFT architecture for Cl− ions sensing which yielded a sensitivity of 4%/ppm to ultrafine ionic concentrations, over an order of magnitude lower than the range identified to cause corrosion in aircraft structures.

Aircraft Fuselage Corrosion Detection Using Artificial Intelligence

Corrosion identification and repair is a vital task in aircraft maintenance to ensure continued structural integrity. Regarding fuselage lap joints, typically, visual inspections are followed by non-destructive methodologies, which are time-consuming. The visual inspection of large areas suffers not only from subjectivity but also from the variable probability of corrosion detection, which is aggravated by the multiple layers used in fuselage construction. In this paper, we propose a methodology for automatic image-based corrosion detection of aircraft structures using deep neural networks. For machine learning, we use a dataset that consists of D-Sight Aircraft Inspection System (DAIS) images from different lap joints of Boeing and Airbus aircrafts. We also employ transfer learning to overcome the shortage of aircraft corrosion images. With precision of over 93%, we demonstrate that our approach detects corrosion with a precision comparable to that of trained operators, aiding to reduce the uncertainties related to operator fatigue or inadequate training. Our results indicate that our methodology can support specialists and engineers in corrosion monitoring in the aerospace industry, potentially contributing to the automation of condition-based maintenance protocols.