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Spatial and temporal deep learning algorithms for defect segmentation in infrared thermographic imaging of carbon fibre-reinforced polymers
Get PDFFor non-destructive evaluation, the segmentation of infrared thermographic images of carbon fibre composites is a critical task in material characterisation and quality assessment. This paper presents a study on the application of image processing techniques, particularly adaptive thresholding, and advanced neural network models, including U-Net, DeepLabv3, and BiLSTM, for the segmentation of infrared images. This work introduces the innovative combination of DeepLabv3 and BiLSTM applied in infrared images of carbon fibre-reinforced polymer samples for the first time, proposing it as a novel approach for enhancing the accuracy of segmentation tasks. An experimental comparison of these models was conducted to assess their effectiveness in identifying artificial defects in these images. The performance of each model was evaluated using the F1-Score and Intersection over Union (IoU) metrics. The results demonstrate that the proposed combination of DeepLabv3 and BiLSTM outperforms other methods, achieving an F1-Score of 0.96 and an IoU of 0.83, showcasing its potential for advanced material analysis and quality control.National Council for Scientific and Technological Development, Coordenação de Aperfeicoamento de Pessoal de Nível SuperiorThis study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code [001] and by the National Council for Scientific and Technological Development - Brazil (CNPq) – Finance Codes [407140/2021–2] and [312530/2023–4].Nondestructive Testing and Evaluatio
High-performance diamond “Supertools” with extreme tool-life
Get PDFThe use of diamond as a cutting tool is pervasive in modern ultra-high-precision machining applications, particularly for generating sub-micron accurate features through the Single Point Diamond Machining (SPDM) method. Beyond SPDM, diamond is also widely employed in contact profilometry (imaging), nanoindentation, nanoimpact, nanoscratching, and lithography applications.
Interestingly, a particular type of diamond, commonly used in what the fabrication industry calls “supertools,” consistently demonstrates a lifespan up to 300% longer than that of standard diamond tools. Despite this remarkable performance, the reasons behind the enhanced durability of these unique diamond tools have remained unclear.
This paper provides the first experimental explanation for the exceptional properties of these “supertools”. Using Fourier Transform Infrared Spectroscopy (FTIR), we establish that such diamond possess higher overall concentration of nitrogen, particularly Defect Type A (type IaA) and Defect Type C (type Ib). Counterintuitively, they also exhibit lower residual stresses, as revealed through cross-polar examination. Moreover, the diamond tip misalignment error, estimated using Laue backscattering analysis, was found to be insignificant in governing the tool wear resistance. These findings suggest that the wear resistance of natural diamonds can be predicted by screening for high levels of nitrogen defects (combination of Type A and Type C). This insight offers valuable potential for selecting superior diamonds for high-value manufacturing.This work was carried out under Cranfield's IMRC #122. SG acknowledges the financial support provided by the UKRI via Grant No. EP/T024607/1. GLWC acknowledges the financial support by Research Ireland via Grant No. SFI 20/US/3681 and the HORIZON-EIC-2021-PATHFINDEROPEN-01 No. 101046693, SSLiP project funded by the European Union.Diamond and Related Material
Review on laser initiation of energetic materials
Get PDFLaser initiation of energetic materials has been an interesting and promising research area due to its safety and reliability features and its great potential for initiation device miniaturisation. This article has critically reviewed its research in terms of the effects of essential initiation parameters, including laser wavelength, optical absorption and laser power on its performance. It aims to show the research and development of laser ignition initiation applications, in particular explosive devices. In addition, this article may also give guidance and recommendations for future research and development in laser initiation applications to energetic materials.Propellants, Explosives, Pyrotechnic
High-temperature corrosion behaviour of stainless steel welds in molten NaNO3-KNO3-KCl environment for concentrated solar power
Get PDFThe use of fossil fuels has caused adverse effects, notably carbon emissions and climatic change. Thermal energy storage (TES) systems can be used to store energy and, when integrated with CSP, can help mitigate the intermittency of renewable sources. Higher TES working temperatures correspond to higher efficiencies which may lower costs. Various substances have been proposed as TES media; molten salts (MS) emerge as one of the most attractive options due to their stability at high temperatures. However, they can accelerate corrosion on materials, and therefore, compatibility between materials and media is crucial. Additionally, the impact of MS on welds (or welded components) has received less attention than for parent material. To address this research gap, this study compares the corrosion behaviour of plain and welded SS316L and SS304L in a mixture of 10NaNO3-78KNO3-12KCl mol% at 600 °C for up to 500 h. The samples were analysed by dimensional metrology (DM), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS). Dimensional metrology shows a higher metal loss and sound metal loss for SS304L than SS316L. SEM micrographs showed the formation of non-adherent corrosion products on alloys surfaces. The EDS shows a selective dissolution of Fe and Cr, along with Na, K and Cl penetration into the alloy. Comparing the results, the welded areas experienced higher metal loss as compared to plain alloy samples. Another key observation was that the SS304L samples displayed lower overall corrosion resistance than SS316L.Journal of Energy Storag
Uncovering the feasibility of using live Chlorella microbiomes in domestic and industrial wastewater treatment: insights into monoculture and synergistic mixed co-cultured system
Get PDFIn recent years, numerous innovative technologies have emerged for algal bioremediation aimed at achieving clean water. Given the diverse functionalities of algae, algal bioremediation presents a viable alternative to conventional wastewater treatment systems. This study specifically emphasizes the use of Chlorella-based algal remediation when integrated with other microbial cultures for clean water applications. Our research provides a thematic review of the real-time integration of Chlorella and its co-cultures with other heterotrophic microbes in the treatment of domestic and industrial wastewater. While many review articles discuss the role of various microalgae species in wastewater treatment generally, to the best of our knowledge, no comprehensive review has documented the use of live algal systems, specifically focusing on a consortium of Chlorella algae, Chlorella bacteria, and Chlorella fungi for pollutant removal in wastewater treatment. This review primarily investigates the mechanisms by which live algal cell consortia—both single cultures and co-cultures—remove biochemical oxygen demand (BOD), chemical oxygen demand (COD), nitrogen, phosphorus, and heavy metals from wastewater. Additionally, the review addresses important observations concerning the characteristics of consortia, optimal growth conditions, the interactions between algae and contaminants, and the use of molecular diagnostic techniques such as PCR, FISH, and metagenomics. Our findings indicate that heterotrophic systems consisting of Chlorella and bacteria demonstrate higher treatment efficacy compared to systems made up of Chlorella and fungi.This study was supported through the AMRITA Seed Grant (Proposal ID: ASG2022146).Journal of Industrial and Engineering Chemistr
Structural sizing and mass estimation of strut- and truss-braced wings: the effects of moderate to high aspect ratios and novel technologies
No full textLoughlan, Joseph - Associate SupervisorThe aviation sector aims for more efficient and environmentally friendly novel transport
aircraft. For this aim, sensitive and rapid conceptual or early preliminary design stage
methods are vital to accurately analysing and comparing potential candidates. Thus, this
PhD thesis presents a comprehensive, physics-based (quasi-analytical) structural sizing
and mass estimation method for conventional and innovative aircraft wings. The method
is unique due to its computational efficiency and ability to cover a wide spectrum of wings,
ranging from moderate to ultra-high aspect ratios, accommodating new wing
configurations such as strut- and truss-braced wings with composite or metallic
materials, and incorporating emerging propulsion technologies such as hydrogen,
electric and distributed propulsion systems.
A key distinguishing feature of this study is the exclusive focus on the load-carrying
structures of an aircraft wing: the wing boxes, struts, juries, and offsets. Validation was
an integral part of the model's development, tested against data from 14 aircraft: 13 real
aircraft with aluminium wing boxes and an aircraft with a high aspect ratio truss-braced
wing and a composite wing box. The results show the model's high accuracy and
correlation with actual wing group mass data, featuring an average error of -2.22% and
a standard error of 1.74%.
Simultaneously, the thesis offers comprehensive literature reviews and identifies
significant gaps in the field. It provides thorough parametric and comparative studies to
increase the credibility of the presented methods and investigate the effect of design
decisions on the mass of the studied aircraft wings. Four hundred eighty-three loading
cases are examined to detect the critical ones that drive the wing components' structural
sizing. A reduced number of nine loading cases is introduced, reducing the
computational time by thirtyfold. Furthermore, primary optimisation studies are
conducted with the developed models for optimum strut-wing attachment and engine
locations, considering different engine counts.PhD in Aerospac
Benign/Cancer diagnostics based on X-ray diffraction: comparison of data analytics approaches
Get PDFThis article belongs to the Special Issue Application of Biostatistics in Cancer ResearchBackground/Objectives: With the number of detected breast cancer cases growing every year, there is a need to augment histopathological analysis with fast preliminary screening. We examine the feasibility of using X-ray diffraction measurements for this purpose. Methods: In this work, we obtained more than 6000 diffraction patterns from 211 patients and examined both standard and custom-developed methods, including Fourier coefficient analysis, for their interpretation. Various preprocessing steps and machine learning classifiers were compared to determine the optimal combination. Results: We demonstrated that benign and cancerous clusters are well separated, with specificity and sensitivity exceeding 0.9. For wide-angle scattering, the two-dimensional Fourier method is superior, while for small angles, the conventional analysis based on azimuthal integration of the images provides similar metrics. Conclusions: X-ray diffraction of biopsy tissues, supported by machine learning approaches to data analytics, can be an essential tool for pathological services. The method is rapid and inexpensive, providing excellent metrics for benign/cancer classification.Cancer
Machine Learning driven complex network analysis of transport systems
Get PDFA complex network is a system of interconnected nodes linked by edges, exhibiting non-trivial structural features such as community structure or scale-free distributions. This study develops a novel and generic Machine Learning-driven framework that integrates Complex Network Theory and Machine Learning methods for a comprehensive and multifaceted analysis of transport systems. Specifically, four key functional development and analysis are undertaken: 1) Network analysis, using complex network indicators to study the static properties of the transport systems; 2) Network clustering, employing K-means and hierarchical clustering methods to identify underlying community structures; 3) Network resilience, examining the networks' dynamic characteristics and structural evolution under escalating node attacks to evaluate their robustness; 4) Link and feature prediction, developing Graph Convolutional Networks (GCNs) and Multi-Layer Perceptron (MLP) models to predict hidden links and features. The proposed framework is subsequently applied to two distinct transport systems, namely, the China railway network and the Paris multi-modal transport system. The complex network analysis reveals distinct complex network features in network scale, density, and efficiency, yet both demonstrate a power-law distribution. The clustering analysis based on various node and edge properties exhibits a pattern of concentric circles, radiating outward from the urban to peripheral cities in China railway network, while a high density of short-distance connections within central Paris and a prevalence of long-distance connections in the outskirts. The network attack simulations show fine resilience of the Parisian multi-modal system and low resilience of the China railway network. For link prediction, an encoder-decoder model based on GCN and multiple MLPs are developed for various scenarios. The results for the China railway network reveal critical interregional links, emphasizing the need to strengthen regional connectivity, such as expanding the high-speed railway between Hainan Island and the mainland, and establishing a major transportation artery running from south to north. In the Paris transport system, this study predicts an interesting link extending from southern Paris eastward toward northern Seine-et-Marne, indicating a demand for a direct connection. For both networks, the hidden links are largely concentrated in more developed areas, likely driven by strong economic and social interaction demands, highlighting the need for more balanced transport network development. Overall, the results of this study align closely with existing literature and official transport development plans. This research contributes to the theoretical development in Complex Network Analysis using Machine Learning and offers valuable insight to improve the two transport systems.Journal of Transport Geograph
Generalising rescue operations in disaster scenarios using drones: a lifelong reinforcement learning approach
No full textSearch and rescue (SAR) operations in post-earthquake environments are hindered by unseen environment conditions and uncertain victim locations. While reinforcement learning (RL) has been used to enhance unmanned aerial vehicle (UAV) navigation in such scenarios, its limited generalisation to novel environments, such as post-disaster environments, remains a challenge. To deal with this issue, this paper proposes an RL-based framework that combines the principles of lifelong learning and eligibility traces. Here, the approach uses a shaping reward heuristic based on pre-training experiences obtained from similar environments to improve generalisation, and simultaneously, eligibility traces are used to accelerate convergence of the overall approach. The combined contributions allows the RL algorithm to adapt to new environments, whilst ensuring fast convergence, critical for rescue missions. Extensive simulation studies show that the proposed framework can improve the average reward return by 46% compared to baseline RL algorithms. Ablation studies are also conducted, which demonstrate a 23% improvement in the overall reward score in environments with different complexities and a 56% improvement in scenarios with varying numbers of trapped individuals.Drone
Thermo-mechanical and curing behaviour of epoxy/dicarboxylic acid vitrimers
Get PDFAyre, David - Associate Supervisor
Thakur, Vijay Kumar - Associate SupervisorThis research delineates the complex relationships between the chemical
evolution processes and the physical behaviour of vitrimeric materials,
particularly focusing on the transesterification bond exchange mechanism. The
curing behaviour, stress relaxation, and high-temperature viscoelastic behaviour
of dicarboxylic acid -bisphenol A diglycidyl ether vitrimers are investigated, with
respect to the influence of catalysts content and dicarboxylic acid chain length.
The influence of catalyst concentration and dicarboxylic acid chain length on the
glass transition temperature and bond exchange rate is significant. A notable
finding is the substantial decrease in both activation energy from 120 to
74 /mol and glass transition temperature from 36°C to 6°C as the carbon chain
length increases from 6 to 14 carbons, due to enhanced monomer flexibility. The
catalyst and dicarboxylic acid structure play a crucial role in polymerisation,
significantly affecting the cure kinetics of the process and, consequently, material
stability and processing. The cure kinetics of epoxy-dicarboxylic acid systems are
described using a diffusion limitation modified autocatalytic model, showcasing
an average 84% goodness of fit, highlighting its effectiveness in understanding
these systems. The study investigates temperature-dependent thermo-
mechanical properties and thermal degradation, revealing that the speed of
formation and extent of crosslinks are influenced by temperature, which in turn
affects thermomechanical properties, and also shows that mechanical properties
increase at higher temperature, along with enhanced thermal stability as the
length of the dicarboxylic acids increases. This study provides a robust foundation
for future research endeavours, aiming to optimise vitrimer properties for diverse
and efficient applications including advanced composites in aerospace and
automotive sectors, self-healing materials, eco-friendly recyclable thermosets,
and adaptable biomedical devices.PhD in Manufacturin