There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

This chapter introduces the finite element method (FEM) as a tool for solution of classical electromagnetic problems. Although we discuss the main points in the application of the finite element method to electromagnetic design, including formulation and implementation, those who seek deeper understanding of the finite element method should consult some of the works listed in the bibliography section.

Introduction to the Finite Element Method

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

This study provides a bibliographic review in the broad field of green composites seeking-out for materials with a potential to be applied in the near future on automotive body panels. Hereupon, materials deriving from renewable resources will be preferred as opposed to the exhaustible fossil products. With the technical information of bio-polymers and natural reinforcements a database was created with the mechanical performance of several possible components for the prospect green composite. Following the review, an assessment is performed where aspects of suitability for the candidate elements in terms of mechanical properties are analyzed. In that section, renewable materials for matrix and reinforcement are screened accordingly in order to identify which hold both adequate strength and stiffness performance along with affordable cost so as to be a promising proposal for a green composite.

Green composites: A review of adequate materials for automotive applications

http://jultika.oulu.fi/files/nbnfi-fe2017112150815.pdf

One-part alkali-activated materials : a review

This article investigated the mechanical performance and corrosion behaviour of a diffusion-bonded A5083 aluminium/A36 mild steel dissimilar joint with a Gallium (Ga) interlayer. The bonding parameters were the bonding temperature (525 and 550 °C), holding time (60 and 120 min) and surface roughness (800 and 1200 grit). Property characterisation was achieved using Scanning Electron Microscopy (SEM), Energy Dispersive X-ray (EDX) analysis, Vickers microhardness tester, Izod impact tester and potentiodynamic polarisation testing. The results revealed that the significance of the bonding parameters was in the order bonding temperature > surface roughness > holding time. Increasing the bonding temperature resulted in an increase in the impact strength and a corresponding reduction in the corrosion rate and microhardness. However, increasing the grit size decreased the microhardness and a corresponding increase in the impact strength and corrosion rate. The impact strength and corrosion rate decreased with the increasing holding time while the microhardness followed a reverse trend. It was also discovered that incorporating the Ga interlayer resulted in a 67.9% improvement in the degradation rate.

/pdf/mechanical-performance-and-corrosion-behaviour-of-diffusion-21tzs7d9.pdf

Mechanical Performance and Corrosion Behaviour of Diffusion-Bonded A5083 Aluminium and A36 Mild Steel with Gallium Interlayer

This work was conducted as part of the European Union project ARTIMA and it investigates the effectiveness of wavelets in damage detection. It examines whether a prior wavelet decomposition of Lamb wave responses facilitates the classification of structural damage. The structure in question is an aluminium plate with viscoelastic-mounted transducers, and the damage took the form of saw cuts on the plate. The novelty detection technique of outlier analysis was used to classify the damage.

Damage Detection Using Prior Wavelet Decompositions

29 , Al 2O3 powder/epoxy and the suggested analytical solution for static analysis of composite plates were presented using general classical laminated plate theory. The Navier solutions are limited to simply support rectangular plates using static analysis. The results show that the effect of the deflections and stresses on the plate thickness-to-length ratio, aspect ratio, modulus ratio, fiber orientation and the number of layers were observed. The deflection and stresses on laminated composite plate decreases with the increase in the number of layers, fiber orientation, and thickness of the laminated plates. These results indicate that the improvements in mechanical properties for aircraft body applications were achieved.

In this study, the behaviour of laminated composite plates, fiber Kevlar

This study presents a novel method for damage detection and identification in unmanned aerial vehicles (UAVs) using vibration data gathering and processing technologies based on deep learning. To conduct the study, a quad-rotor UAV was manufactured, and a vibration data acquisition system was developed to collect vibration data along three axes under normal and three damage scenarios. Empirical mode decomposition (EMD) was employed to reduce high-frequency noise in the signals, and the root mean square error (RMSE) feature was utilised to select the Y-axis acceleration data, which exhibits significant changes across different damage cases. Finally, a convolutional neural network was used to identify the damage based on the vibration data. Experimental results demonstrate that the proposed method achieved 97.5% accuracy using selected and noise-reduced Y-axis acceleration data, thereby indicating its usefulness in diagnosing damage types in multi-rotor UAVs. 

Structural fault diagnosis of UAV based on convolutional neural network and data processing technology

In this paper, a numerical simulation on a new fabrication miniature composite fuselage structure- a woven composite laminated with an adhesively bonded butt joint under the axial compression loading is presented. A FEA via Abaqus/Explicit was utilized to capture the complete compressive responses to predict the crushing behaviour and its mechanical strength from the initial compression loading till its final failure mode. A woven C-glass fiber/epoxy 200 g/m2 composite laminated [908] with the orthotropic elastic material properties is modeled as a continuum composite layup in the proposed numerical model. The adhesively bonded joint progression was modeled using the cohesive element technique. The proposed model was used to observe the crushing load and collapse modes under the axial compression impact. The finite element results showed a good agreement with the experimental results under the actual quasi-static tests. The validation code was extended to further undergo parametric studies in order to visualise the effect of angle orientations and special laminate cases as to attain the optimum design criteria for the proposed composite structure.

/pdf/parametric-study-on-cohesive-element-for-composite-fuselage-43xyy0tumt.pdf

Faizal Mustapha

Papers

Mechanical Performance and Corrosion Behaviour of Diffusion-Bonded A5083 Aluminium and A36 Mild Steel with Gallium Interlayer

Damage Detection Using Prior Wavelet Decompositions

In this study, the behaviour of laminated composite plates, fiber Kevlar

Structural fault diagnosis of UAV based on convolutional neural network and data processing technology

Parametric Study on Cohesive Element for Composite Fuselage Model