Nondestructive Testing in Composite Materials

In this era of technological progress and given the need for welfare and safety, everything that is manufactured and maintained must comply with such needs. We would all like to live in a safe house that will not collapse on us. We would all like to walk on a safe road and never see a chasm open in front of us. We would all like to cross a bridge and reach the other side safely. We all would like to feel safe and secure when taking a plane, ship, train, or using any equipment. All this may be possible with the adoption of adequate manufacturing processes, with non-destructive inspection of final parts and monitoring during the in-service life of components. Above all, maintenance should be imperative. This requires effective non-destructive testing techniques and procedures. This Special Issue is a collection of some of the latest research in these areas, aiming to highlight new ideas and ways to deal with challenging issues worldwide. Different types of materials and structures are considered, different non-destructive testing techniques are employed with new approaches for data treatment proposed as well as numerical simulations. This can serve as food for thought for the community involved in the inspection of materials and structures as well as condition monitoring

Defect Detection in Synthetic Fibre Ropes using Detectron2 Framework

Fibre ropes with the latest technology have emerged as an appealing alternative to steel ropes for offshore industries due to their lightweight and high tensile strength. At the same time, frequent inspection of these ropes is essential to ensure the proper functioning and safety of the entire system. The development of deep learning (DL) models in condition monitoring (CM) applications offers a simpler and more effective approach for defect detection in synthetic fibre ropes (SFRs). The present paper investigates the performance of Detectron2, a state-of-the-art library for defect detection and instance segmentation. Detectron2 with Mask R-CNN architecture is used for segmenting defects in SFRs. Mask R-CNN with various backbone configurations has been trained and tested on an experimentally obtained dataset comprising 1,803 high-dimensional images containing seven damage classes (loop high, loop medium, loop low, compression, core out, abrasion, and normal respectively) for SFRs. By leveraging the capabilities of Detectron2, this study aims to develop an automated and efficient method for detecting defects in SFRs, enhancing the inspection process, and ensuring the safety of the fibre ropes.Comment: 12 pages, 7 figures, 4 table

Applications of Computer Vision Technologies of Automated Crack Detection and Quantification for the Inspection of Civil Infrastructure Systems

Many components of existing civil infrastructure systems, such as road pavement, bridges, and buildings, are suffered from rapid aging, which require enormous nation\u27s resources from federal and state agencies to inspect and maintain them. Crack is one of important material and structural defects, which must be inspected not only for good maintenance of civil infrastructure with a high quality of safety and serviceability, but also for the opportunity to provide early warning against failure. Conventional human visual inspection is still considered as the primary inspection method. However, it is well established that human visual inspection is subjective and often inaccurate. In order to improve current manual visual inspection for crack detection and evaluation of civil infrastructure, this study explores the application of computer vision techniques as a non-destructive evaluation and testing (NDE&T) method for automated crack detection and quantification for different civil infrastructures. In this study, computer vision-based algorithms were developed and evaluated to deal with different situations of field inspection that inspectors could face with in crack detection and quantification. The depth, the distance between camera and object, is a necessary extrinsic parameter that has to be measured to quantify crack size since other parameters, such as focal length, resolution, and camera sensor size are intrinsic, which are usually known by camera manufacturers. Thus, computer vision techniques were evaluated with different crack inspection applications with constant and variable depths. For the fixed-depth applications, computer vision techniques were applied to two field studies, including 1) automated crack detection and quantification for road pavement using the Laser Road Imaging System (LRIS), and 2) automated crack detection on bridge cables surfaces, using a cable inspection robot. For the various-depth applications, two field studies were conducted, including 3) automated crack recognition and width measurement of concrete bridges\u27 cracks using a high-magnification telescopic lens, and 4) automated crack quantification and depth estimation using wearable glasses with stereovision cameras. From the realistic field applications of computer vision techniques, a novel self-adaptive image-processing algorithm was developed using a series of morphological transformations to connect fragmented crack pixels in digital images. The crack-defragmentation algorithm was evaluated with road pavement images. The results showed that the accuracy of automated crack detection, associated with artificial neural network classifier, was significantly improved by reducing both false positive and false negative. Using up to six crack features, including area, length, orientation, texture, intensity, and wheel-path location, crack detection accuracy was evaluated to find the optimal sets of crack features. Lab and field test results of different inspection applications show that proposed compute vision-based crack detection and quantification algorithms can detect and quantify cracks from different structures\u27 surface and depth. Some guidelines of applying computer vision techniques are also suggested for each crack inspection application

Condition Monitoring Technologies for Steel Wire Ropes – A Review

In this research, we review condition-monitoring technologies for offshore steel wire ropes (SWR). Such ropes are used within several offshore applications including cranes for load handling such as subsea construction at depths up to 3-4000 meters, drilling lines, marine riser tensioner lines and anchor lines. For mooring, there is a clear tendency for using fiber ropes. Especially for heavy-lift cranes and subsea deployment, winches with strong ropes of up to 180 mm in diameter may be required, which has a considerable cost per rope, especially for large water depths. Today’s practice is to discard the rope after a predetermined number of uses due to fatigue from bending over sheaves with a large safety factor, especially for systems regulated by active heave compensation (AHC). Other sources of degradation are abrasion, fretting, corrosion and extreme forces, and are typically accelerated due to undersized or poorly maintained sheaves, groove type, lack of lubrication and excessive load. Non-destructive testing techniques for SWR have been developed over a period of 100 years. Most notably are the magnetic leakage techniques (electromagnetic methods), which are widely used within several industries such as mining and construction. The content reviewed in this research is primarily the developments the last five years within the topics of electromagnetic method, acoustic emissions (AE), ultrasound, X- and γ-rays, fiber optics, optical and thermal vision and current signature analysis. Each technique is thoroughly presented and discussed for the application of subsea construction. Assessments include ability to detect localized flaws (i.e. broken wire) both internally and externally, estimated loss of metallic cross sectional area, robustness with respect to the rough offshore environment, ability to evaluate both rope and end fittings, and ability to work during operation

Condition monitoring of fibre ropes using machine learning

The application of fibre ropes in offshore lifting operations has significant potential for further development. With minimum breaking loads (MBL) equivalent to steel wire at similar diameters and almost neutral buoyancy in water, it is in theory possible to reach depths exceeding 3000 m with smaller cranes and vessels, representing substantial savings in not only potential operation costs. However, with fibre ropes there are different requirements and standards to consider with regards to condition monitoring, maintenance and retirement criteria. Safe and reliable operations are paramount in the offshore sector and any incidents that occur during offshore lifting would not be only significantly damaging financially but could potentially lead to loss of life. Current standards for fibre rope condition monitoring originate in mooring applications, and are based on manual inspection for retirement and re-certification. There is significant room for developments in methods that can aid the inspection process. To address this problem, computer vision and thermal monitoring methods for fibre ropes are developed and experimentally investigated at the Mechatronics Innovation Lab in Grimstad, Norway. The methods are used to monitor changes in fibre rope condition during cyclic-bend-over-sheave testing and to find relevant condition indicators that give more information regarding the condition and remaining useful life of the fibre rope. In addition, the data recorded is used to form machine learning models that both classify rope condition and predict the remaining life of fibre ropes during CBOS testing. The expected outcome is to use physics-based machine learning methods to improve both condition classification and remaining useful life estimation of  bre ropes used in offshore lifting operations. In the appended papers at the end of this thesis, the proposed methods have been experimentally investigated and validated through cyclic-bend-over-sheave experiments performed at the Mechatronics Innovation Lab and further data analysis performed at the University of Agder, Norway and at divis in Dortmund, Germany.publishedVersio

The condition monitoring of damaged steel structures.

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN012487 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Damage accumulation in high performance synthetic fibre ropes

This thesis presents the results of an investigation into the process of damage and failure in small diameter high performance synthetic fibre ropes namely Dyneema, Vectran and Technora ropes. This study was prompted by a series of fatal accidents on paragliders as a result of the line failure. All the different rope materials, including the rope with cover, without cover and the core with different number of strands, have been tensile tested. The transfer of loading and subsequent damage in different rope constituents, fibres and strands, are also discussed. The residual strength of the rope after static and cyclic preloading regimes is discussed and possible mechanisms for the damage accumulation in the rope are given. The acoustic emission monitoring of the tensile and residual strength tests shows distinctive differences between the different types of rope and permits the identification of characteristic effects of preloading on the tensile damage and failure mechanisms of all three materials. The process of damage in the Dyneema and Vectran is similar, in which damage progresses in steps during the loading history whereas Technora rope accumulates gradual increase in damage until the catastrophic failure. The application of the static preloading improves the strength of Dyneema and Vectran ropes whereas it deteriorates the mechanical properties of Technora rope. The cyclic response of Dyneema rope shows a dramatic downturn at lives in excess of 1000 cycles, but moderate cyclic loading improves the strength. The variation in surface temperature of Dyneema rope during tensile loading has been measured analysed and related to the process of damage. Dyneema fibres melt and fuse together under loading, since Dyneema is disadvantaged by its low melting temperature. Rope on rope abrasion tests, carried out on covered and uncovered Dyneema and Technora ropes, show that Dyneema rope has superior abrasion properties compared to Technora. This is due to the low compression properties of Technora, as abrasion process involves compressing the fibres. The effect of exposure to different environments, including natural weathering, -22'C, +54'C and seawater on tensile performance is discussed. The tensile properties of the Dyneema ropes are little affected by the environmental conditioning except the effect of synthetic sea water, in which case the salt crystals damage the rope fibres, once the water has evaporated

Condition monitoring and maintenance for fibre rope moorings in offshore wind

The FIRM project aims to develop innovative mooring systems for floating wind farms based on fibre ropes, including new and more efficient methods for installation, condition monitoring, maintenance, and decommissioning. The project shall deliver designs for three different mooring systems. This document describes the contents of work package H7 in the FIRM project,publishedVersio

JUNO Conceptual Design Report

The Jiangmen Underground Neutrino Observatory (JUNO) is proposed to determine the neutrino mass hierarchy using an underground liquid scintillator detector. It is located 53 km away from both Yangjiang and Taishan Nuclear Power Plants in Guangdong, China. The experimental hall, spanning more than 50 meters, is under a granite mountain of over 700 m overburden. Within six years of running, the detection of reactor antineutrinos can resolve the neutrino mass hierarchy at a confidence level of 3-4$\sigma$, and determine neutrino oscillation parameters $\sin^2\theta_{12}$, $\Delta m^2_{21}$, and $|\Delta m^2_{ee}|$ to an accuracy of better than 1%. The JUNO detector can be also used to study terrestrial and extra-terrestrial neutrinos and new physics beyond the Standard Model. The central detector contains 20,000 tons liquid scintillator with an acrylic sphere of 35 m in diameter. $\sim$17,000 508-mm diameter PMTs with high quantum efficiency provide $\sim$75% optical coverage. The current choice of the liquid scintillator is: linear alkyl benzene (LAB) as the solvent, plus PPO as the scintillation fluor and a wavelength-shifter (Bis-MSB). The number of detected photoelectrons per MeV is larger than 1,100 and the energy resolution is expected to be 3% at 1 MeV. The calibration system is designed to deploy multiple sources to cover the entire energy range of reactor antineutrinos, and to achieve a full-volume position coverage inside the detector. The veto system is used for muon detection, muon induced background study and reduction. It consists of a Water Cherenkov detector and a Top Tracker system. The readout system, the detector control system and the offline system insure efficient and stable data acquisition and processing.Comment: 328 pages, 211 figure