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Graphical abstract, Highlights • High-speed imaging captured de-agglomeration and dispersion of oxide aggregates. • De-agglomeration occurred from both bulk and surface of the aggregate. • Floating microbubble cluster drones produced fine suspension of loose agglomerates. • Ultrasonic capillary effect promoted de-agglomeration of particles from within. • Subharmonic and low order ultra-harmonic bubble emissions induced oxide breakage., Ultrasonic de-agglomeration and dispersion of oxides is important for a range of applications. In particular, in liquid metal, this is one of the ways to produce metal-matrix composites reinforced with micron and nano sized particles. The associated mechanism through which the de-agglomeration occurs has, however, only been conceptualized theoretically and not yet been validated with experimental observations. In this paper, the influence of ultrasonic cavitation on SiO2 and MgO agglomerates (commonly found in lightweight alloys as reinforcements) with individual particle sizes ranging between 0.5 and 10 μm was observed for the first time in-situ using high-speed imaging. Owing to the opacity of liquid metals, a de-agglomeration imaging experiment was carried out in de-ionised water with sequences captured at frame rates up to 50 kfps. In-situ observations were further accompanied by synchronised acoustic measurements using an advanced calibrated cavitometer, to reveal the effect of pressure amplitude arising from oscillating microbubbles on oxide de-agglomeration. Results showed that ultrasound-induced microbubble clusters pulsating chaotically, were predominantly responsible for the breakage and dispersion of oxide agglomerates. Such oscillating cavitation clusters were seen to capture the floating agglomerates resulting in their immediate disintegration. De-agglomeration of oxides occurred from both the surface and within the bulk of the aggregate. Microbubble clusters oscillating with associated emission frequencies at the subharmonic, 1st harmonic and low order ultra-harmonics of the driving frequency were deemed responsible for the breakage of the agglomerates.

There has been a lot of work done on the analysis of Gaussian loading analysis perhaps because its occurrence is more common than non-Gaussian loading problems. It is nevertheless known that non-Gaussian load occurs in many instances especially in various forms of transport, land, sea and space. Part of the challenge with non-Gaussian loading analysis is the increased number of variables that are needed to model the loading adequately. Artificial neural network approach provides a versatile means to develop models that may require many input variables in order to achieve applicable predictive generalisation capabilities. Artificial neural network has been shown to perform much better than existing frequency domain methods for random fatigue loading under stationary Gaussian load forms especially when mean stress effects are included. This paper presents an artificial neural network model with greater predictive capability than existing frequency domain methods for both Gaussian and non-Gaussian loading analysis. Both platykurtic and leptokurtic non-Gaussian loading cases were considered to demonstrate the scope of application. The model was also validated with available SAE experimental data, even though the skewness and kurtosis of the signal in this case were mild.

The use of artificial intelligence especially based on artificial neural networks (ANN) is now prevalent in many fields of data analysis and interpretation. There have been a number of papers published in the literature on the use of ANN for fatigue characterisation. Most of these have however been developed for rather focussed application with limited capability for fatigue life prediction for a broad scope of material and loading conditions. The authors recently presented a uniquely generalised ANN model that is capable of making fatigue life prediction for a broad range of material fatigue properties and loading spectral forms. The model was developed using simulated data albeit subject to conceivable constraints between possible materials properties and load forms. This paper presents a validation of the ANN model using a Society of Automotive Engineers (SAE) random fatigue loading experimental test data. The capabilities and potentials of the model are demonstrated by comparison with the SAE random load fatigue test results and with results obtained from other predictive methods. The performance of the ANN is highly encouraging as a general tool for random loading fatigue analysis.

The objective of this paper is to measure the dynamic response transmissibility and fatigue life of vacuum cast Polyurethane (PU) polymer material used in the construction of automotive lamps. Polymers used in automotive lamps, if mass produced, are injection moulded. For ultra-low volume production, however, the cost of tooling for these traditional manufacturing methods is often very high. Hence, alternative approaches such as Silicone Tool – Vacuum (STV) casting are used. The parts produced using alternative approaches may have inferior dynamic performance. The specimens for dynamic response and fatigue testing were cut-out from a vacuum cast polyurethane plate. An instrumented beam was mounted on a shaker table and using sine sweep base excitation the dynamic response was measured. Using measured acceleration, response transmissibility was calculated. The bending fatigue properties were measured on a vibration shaker using a 4-point bending based resonance test apparatus. A series of tests were carried out to obtain average performance parameters. The dynamic response shows nonlinearity, as expected of polymers, and a large variation in dynamic properties is observed. The obtained fatigue life curve shows significant scatter at 40% fatigue load level. The assessed fracture surface micrograph of the fatigued specimens at three load levels shows surface tear at 80% and 60% load levels. Overall, the Vacuum cast polyurethane shows less fatigue performance than the conventional polymers of automotive lamps.

The objective of this paper is to develop a robust numerical fatigue life prediction model for Polymethyl Methacrylate (PMMA) polymer material of automotive lamp components subjected to random vibration loading, with consideration for material non-linearity to reduce number of iterations in the design cycle. The fatigue life based on initial elastic modulus and secant modulus is predicted using ANSYS software and compared to the experimentally obtained fatigue life. Three fatigue life prediction models, Steinberg, Narrow-Band and Wirsching were used. Twelve specimens cut-out from injection moulded optical blades of PMMA were tested to obtain the fatigue life using electrodynamic shaker. The average experimental fatigue life was obtained from the twelve specimens tested. The use of initial elastic range based modulus gives fatigue life that is 43% lower than the experimental result, while for the secant modulus based analysis, the fatigue life accurately matches the experimental result with only 1% difference. Hence, a complete non-linear finite element model may not be necessary to estimate the fatigue life. The numerical result is based on Wirsching fatigue model, which provides a more accurate prediction than Steinberg and Narrow-Band models.

The effect of mean stress is a significant factor in design for fatigue, especially under high cycle service conditions. The incorporation of mean stress effect in random loading fatigue problems using the frequency domain method is still a challenge. The problem is due to the fact that all cycle by cycle mean stress effects are aggregated during the Fourier transform process into a single zero frequency content. Artificial neural network (ANN) has great scope for non-linear generalization. This paper presents artificial neural network methods for including the effect of mean stress in the frequency domain approach for predicting fatigue damage. The materials considered in this work are metallic alloys. The results obtained present the ANN method as a viable approach to make fatigue damage predictions including the effect of mean stress. Greater resolution was obtained with the ANN method than with other available methods.

Machine learning is now applied in virtually every sphere of life for data analysis and interpretation. The main strengths of the method lie in the relative ease of the construction of its structures and its ability to model complex non-linear relationships and behaviours. While application of existing materials have enabled significant technological advancement there are still needs for novel materials that will enable even greater achievement at lower cost and higher effectiveness. The physics underlining the phenomena involved in materials processing and behaviour however still pose considerable challenge and yet require solving. Machine learning can facilitate the achievement of these new aspirations and desires by learning from existing knowledge and data to fill in gaps that have so far been intractable for various reasons including cost and time. This paper reviews the applications of machine learning to various aspects of materials design, processing, characterisation, and some aspects of fabrication and environmental impact evaluation.

A novel test rig for bending fatigue test that based on specimen resonant behaviour has been developed. Determining bending fatigue properties of polymer materials with the standard test systems is challenging, and in some cases results are unattainable. This is particularly true of polymers that exhibit a high level of non-linearity and large deflection. This novel test setup is similar to that of four point bending arrangement resulting in a simple support. The loading is achieved by inertial effect of small masses mounted on the test specimen. A vibration shaker is used to base excite the specimen at the first resonance frequency until it breaks. The proposed test setup reduces the time taken to obtain Stress v/s number of cycles (S/N) curves, typically 1/10th of the universal testing machine based approach. The effect of nonlinearities can be reduced by application of larger loads at higher frequencies using large acceleration and smaller deflection combination. The results based on the proposed approach are in good agreement with tensile fatigue results. It has been successfully used to determine the bending fatigue properties of Polycarbonate (PC) of which determining the tensile fatigue properties were difficult to obtain. The significance of this novel test rig is that it accelerates the fatigue testing and allows the determination of the fatigue properties of some materials that cannot be obtained with existing systems.

The objective of this paper was to model random vibration response of components of an automotive lamp made of Polycarbonate/Acrylonitrile Butadiene Styrene (PC-ABS), Polymethyl methacrylate (PMMA) and Polypropylene 40% Talc filled (PPT40) materials using a nonlinear hyperelastic model. Traditionally, the Rayleigh damping matrix used in the dynamic response analysis is constructed considering linear elastic behaviour based on either initial stiffness or secant stiffness. The performance of linear stiffness matrices is compared in this work with that based on the nonlinear hyperelastic, Mooney-Rivlin model, specifically addressing Rayleigh damping matrix construction. The random vibration responses of 10 samples of each material are measured. The mean square error of acceleration response was used to assess the effectiveness. Considering three materials of study, the hyperelastic model resulted in the reduction of the least square error at best by 11.8 times and at worst by 2.6 times. The Mooney-Rivlin material model based Raleigh damping matrix was more accurate in modelling the dynamic behaviour of components of nonlinear materials and it also represented the manufacturing variabilities more reliably.

Random vibration fatigue loading occurs in automotive, aerospace, offshore and indeed in many structural and machine components. The analysis of these types of problems is often carried out using either time domain or frequency domain methods. Time domain rainflow counting together with Miner’s linear damage accumulation assumption is widely accepted as a method of rationalising stress amplitude and mean stress from random fatigue loading and the damage caused to the component. Frequency domain methods provide a faster alternative for the analysis of the same problem but the results are generally conservative compared to those obtained using time domain methods. This paper presents an artificial neural network (ANN) machine learning approach for the prediction of damage caused by random fatigue loading. The results obtained for ergodic Gaussian stationary stochastic loading is very encouraging. The method embodies rapid analysis as well as better agreement with rainflow counting method than existing frequency domain methods.

Graphical abstract, Highlights • Mechanical properties of extracted intermetallics determined using a nano-indenter. • High speed imaging captures shock waves breaking intermetallics. • Shock waves seen to promote crack growth leading to fragmentation. • In-situ shock wave pressure measurements obtained using a fibre optic hydrophone. • Subharmonic emissions generate repetitive bending and fracture of intermetallics., One of the main applications of ultrasonic melt treatment is the grain refinement of aluminium alloys. Among several suggested mechanisms, the fragmentation of primary intermetallics by acoustic cavitation is regarded as very efficient. However, the physical process causing this fragmentation has received little attention and is not yet well understood. In this study, we evaluate the mechanical properties of primary Al3Zr intermetallics by nano-indentation experiments and correlate those with in-situ high-speed imaging (of up to 1 Mfps) of their fragmentation process by laser-induced cavitation (single bubble) and by acoustic cavitation (cloud of bubbles) in water. Intermetallic crystals were chemically extracted from an Al-3 wt% Zr alloy matrix. Mechanical properties such as hardness, elastic modulus and fracture toughness of the extracted intermetallics were determined using a geometrically fixed Berkovich nano-diamond and cube corner indenter, under ambient temperature conditions. The studied crystals were then exposed to the two cavitation conditions mentioned. Results demonstrated for the first time that the governing fragmentation mechanism of the studied intermetallics was due to the emitted shock waves from the collapsing bubbles. The fragmentation caused by a single bubble collapse was found to be almost instantaneous. On the other hand, sono-fragmentation studies revealed that the intermetallic crystal initially underwent low cycle fatigue loading, followed by catastrophic brittle failure due to propagating shock waves. The observed fragmentation mechanism was supported by fracture mechanics and pressure measurements using a calibrated fibre optic hydrophone. Results showed that the acoustic pressures produced from shock wave emissions in the case of a single bubble collapse, and responsible for instantaneous fragmentation of the intermetallics, were in the range of 20–40 MPa. Whereas, the shock pressure generated from the acoustic cavitation cloud collapses surged up to 1.6 MPa inducing fatigue stresses within the crystal leading to eventual fragmentation.

The objective of this paper is to model and analyse the dynamic response of an automotive lamp assembly. Modern automotive lamp assemblies have complex geometry and are composed of different parts made of polymer materials. As part of design verification, automotive lamp assemblies are subjected to accelerated random vibration tests to assess their integrity over a life-time exposure to mechanical vibration loading. Understanding the dynamic behaviour of the lamp is crucial in the numerical evaluation of the fatigue life. Dynamic analysis involves characterising the modal and harmonic behaviours. In this work, numerical modal properties and harmonic responses were validated using experimental testing. The numerical analysis was carried out using the ANSYS finite element analysis (FEA) software. Experimental modal properties including mode shapes and corresponding frequencies were determined using Polytec PSV-500 Xtra laser scanning head at a frequency range of 10 to 1000Hz. The experimental harmonic transmissibility responses of all the components of the lamp assembly were determined using a vibration shaker. The experimental and numerical mode shapes and responding frequencies obtained in the analyses compared well thus validating the numerical modal model. Furthermore, the mode shapes showed that the lamp assembly was mostly vibrating in bending, therefore subsequent analysis should take this into account. Harmonic response validation showed that the first few numerical resonant frequencies, that dominate the response, compared well with experimental results.

© The Authors, 2020. Ultrasonic cavitation melt treatment (UST) of aluminium alloys has received considerable attention in the metal industry due to its simple and effective processing response. The refined primary intermetallic phases formed in the treated alloys during controlled solidification, govern alloy structural and mechanical properties for applications in the automotive and aerospace industries. Since the UST is performed close to the liquidus temperatures of the alloys, understanding the refinement mechanism of the primary intermetallic phases has been beset by difficulties in imaging and handling of liquid metals. In this paper, the sonofragmentation behaviour of primary intermetallic Al3Zr crystals extracted from the matrix of an Al-3 wt% Zr alloy and fixed on a solid substrate was investigated. The intermetallics were exposed to cavitation action in deionized water at 24 kHz of ultrasound frequency. The fragmentation mechanism from the nearby collapsing cavitation bubbles was studied with in-situ high speed imaging. Results revealed that the main fragmentation mechanism is associated with the propagation of shock wave emissions from the collapsing bubble clouds in the vicinity of the crystal. The mechanical properties of the Al3Zr phase determined previously were used for the fracture analysis. It was found that an Al3Zr intermetallic undergoes low cycle fatigue fracture due to the continuous interaction with the shock wave pressure. The magnitude of the resulting shear stress that leads to intermetallic fragmentation was found to be in the range of 0.6 – 1 MPa.

The object of this paper is to determine the statistics of parameters of hyperelastic models specific to Polybutylene Terephthalate filled with 30% glass fibre (PBT GF30) and Polymethyl Methacrylate (PMMA) materials used in automotive lamps. The hyperelastic behaviour of both materials, a semi-crystalline and an amorphous, is modelled using appropriate hyperelastic models. The stress-strain curves of the materials were measured under uniaxial tension using a non-contact video gauge. Five samples each were tested to measure the effect of manufacturing variability. The model parameter statistics were determined, the mean value of the model parameters were used to construct average stress-strain behavior, which is then compared to the experimental stresses. Among all the models and their associated parameters studied, the 3 - parameter Mooney-Rivlin model provided the most accurate prediction of the behaviour for both materials. The model showed excellent stability and is therefore the most appropriate model to represent variations due to the manufacturing process. The detailed study of the correlation of the model parameters provided a good understanding of how the parameters are related to each other, enabling construction of complete probability distribution functions for further analysis.

A two-dimensional finite element (FE) model has been developed for determining crack opening and closure stresses, with the eventual aim of investigating plasticity induced closure effects on crack growth under variable amplitude loading. An issue with model verification is obtaining accurate experimental values of crack opening and closure loads. Validation was therefore carried out using experimental data from constant amplitude loading tests, recently obtained by the authors[1, 2] where there was good confidence in the accuracy of the opening and closing loads. Elastic–perfect plastic and work hardening material properties were investigated to determine the effect they had on crack growth. The modelling considered long cracks by dividing the crack into consecutive small lengths. For this purpose, the restart capability included in the ABAQUS code was employed. In addition, a mesh refinement strategy was optimised to reduce the memory requirements for the thousands of cycles analysed. This enabled both long crack lengths and small element sizes to be studied which has not been done in the literature before. The FE results were in good agreement with most of the experimental results, and possible reasons are given for some of the minor discrepancies observed.

This study aims to determine the most suitable flow metering device for the characterisation of heavy duty diesel injector behaviour. The study focuses on three commercially available metering devices and the main principles they employ. An experiment was carried out to benchmark the performance of each device’s measurement repeatability in the characterisation of fuel injector behaviour. This study then compares the capabilities and suitability of each for use in a production environment. The comparison was carried out for Delphi Technologies using the new DFi21 heavy duty diesel injector which uses miniaturised hydraulic three way control valve technology.

The estimation of crack growth under variable amplitude loading is complex due to interaction effects such as plasticity, crack tip blunting, residual stresses, crack tip closure and crack tip branching. Crack closure has been identified to be one of the main interaction effects. In order to study the effect of crack closure the authors have previously carried out experimental testing to obtain more accurate measurements of crack opening and closure (Aguilar-Espinosa, 2009, Aguilar-Espinosa et al., 2013). They have also developed two dimensional plane stress Finite Element models utilising high mesh density whilst maintaining the ability to measure crack growth over long crack lengths (Aguilar Espinosa et al. 2017). This initial work has been extended in this paper to examine the effects of single and block overloads and random spectrum loading on crack growth. The crack length distance that is affected by overloads and underloads measured experimentally and predicted numerically are shown to be very close when using cyclic hardening material properties and kinematic hardening. In addition the comparison of experimental and numerical crack growth versus crack length graphs shows good correlation of the crack growth acceleration and retardation after the applied overload which has not been seen previously. These comparisons seem to be a very useful tool to validate numerical models.

There is increasing demand for effective and efficient lightweight structures because of global environmental and resource sustainability concerns. Adhesive bonding has been adopted in many assembly arrangements because of its relative reduction of stress concentration in joints compared to mechanical fasteners. Functionally graded bonded joints presents even greater potentials for reduction of stress concentrations and the tailoring of stress distribution as may be desired in an adhesive layer. This capability provides opportunity for the design of high performance tailored structural assemblies. Although some encouraging analysis and experimental work have been carried out on the development of functionally graded joints, its wide application is still to be realised. This paper reviews the work that has been carried out so far on the method, in terms of analysis, fabrication, experimental testing and application. It also reflects on outstanding issues that need to be resolved in order for wider application to be feasible.

Continuous fiber reinforced metal matrix composites (MMCs) presents the architecture or arrangement of fiber in matrix materials that offer realization of the highest possible mechanical and thermal management properties from the combination. The highest forms of specific stiffness, strength, fatigue and creep resistance are realized in this form of composite compared to particulate and short fiber reinforced MMCs. This chapter presents the types of matrix and fiber materials that are commonly used for the fabrication of long fiber reinforced MMCS. It presents the factors that influence the choice of fiber and matrix for the fabrication and the type of reaction problems that can occur during the high temperature processing that is often required. The types of fabrication methods such as liquid, physical and chemical vapor deposition methods, matrix coated fiber, foil–fiber–foil, and more recently ultrasonic processes are highlighted. The initiatives that have been taken to mitigate deleterious interfacial reaction are discussed. The effects of fiber volume fraction and mismatch of the fiber and the matrix properties on residual stress generated during fabrication and its consequent effects on fatigue and creep are underscored. The potentials of the composite have not been fully realized due to high processing cost. This has reflected in the unstable interests and involvement of manufacturing companies in its production over the years. The chapter lists outstanding suppliers of fibers and long fiber MMCs. Demonstrator components mainly for aerospace and electric power distribution industries are highlighted. Electric power distribution Al/Alumina fiber MMC cables that has become a commercial success case in diverse parts of the world is also presented.

Ceramics have extensively been used for ballistic protection in the last decades. The combination of their mechanical properties makes them very interesting for armouring. Indeed, they exhibit a high hardness, large compression strength, high stiffness and low density. Ceramic armouring plates are commonly manufactured through a sintering process, where ceramic powders are pressed at high temperatures. This manufacturing process tends to limit the size and shape of components and imparts high costs. On the other hand, moulding using a polymer matrix composite provides an alternative process for developing lower cost parts whilst accommodating increased complexity of geometry and size.However, the mechanical behaviour of such a material is not completely known and depends on multiple design parameters: the mechanical properties of the phases, their volume fraction, the size and spatial distributions of the particles, and the adhesion between the components. The objective of the thesis is to evaluate the influence of the main morphological parameters on the overall mechanical properties, emphasising the influence of the particle/matrix adhesion. To do so, both numerical and experimental multiscale analyses of the material under quasi-static and dynamic loadings were carried out.More precisely, static and dynamic properties of the particle-reinforced composite have been determined for different combinations of the design variables. In particular, attention has been dedicated to the particle/matrix decohesion mechanism. Cohesive zone models (CZM) and Finite Fracture Mechanics (FFM) approaches were used to model this phenomenon and a strong effect of the particle size on debonding was observed., Les matériaux céramiques présentent généralement des propriétés mécaniques très intéressantes pour la réalisation de blindages. Ce sont des matériaux très durs et pourtant légers. Les plaques de blindages en céramique sont classiquement mises en forme par pressage à haute température de poudres, ce qui limite la taille et la forme des réalisations tout en impliquant un coût élevé. Une alternative pour produire ces pièces est le moulage d’un composite constitué de particules de céramiques dans une matrice époxy. Ce procédé permet de réduire le coût des pièces tout en autorisant des géométries plus complexes et des dimensions plus importantes.Le comportement mécanique de ce type de matériau dépend de multiples paramètres de conception : propriétés mécaniques des constituants (matrice polymère et particules céramiques), proportion volumique des deux phases, taille et distribution spatiale des particules ou encore l’adhésion entre les constituants. L’objectif de la thèse est d’évaluer l’influence de ces paramètres sur les propriétés d’usage du matériau. Pour ce faire, une analyse multi-échelle du matériau sous sollicitations quasi-statique et dynamique est réalisée.Plus précisément, les propriétés statiques et dynamiques du composite à renforts particulaires ont été déterminées pour différentes combinaisons de ces paramètres de conception. En particulier, le mécanisme de décohésion particule/matrice a été spécifiquement étudié. Les approches de Modèles de Zone Cohésive (CZM) et de Mécanique de la Rupture Finie (FFM) ont été utilisées pour modéliser ce phénomène et un fort effet de taille des particules a été observé.