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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.

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.

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.

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.

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.

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.

Thermal residual stresses arise in long fibre reinforced composites such as metal matrix composites due to the mismatch of the thermal and mechanical properties of the constituents and the change in temperature during processing. This paper presents an inverse axisymmetric model that uses the fibre deformation obtained when the matrix is selectively etched away in order to back calculate the inherent residual stresses. The model is tested using finite element method simulation and also on published experimental data. Although, an approximate inverse solution exists in the literature, there is an ambiguity in the value of Poisson’s ratio to be used which can lead to large errors relative to the full solution presented in this paper. A sensitivity analysis is also carried out to quantify the effect of variability of material properties on the stress values obtained using the solution.

Crack closure and opening stresses have been determined for 6082-T6 aluminium, subjected to single and block overloads and single and block overloads and underloads, using an optical method. The distance between the overloads was varied in order to investigate the effect that crack growth distance between overloads has on crack growth rate. The optical method provided high magnification allowing crack closure away from the crack tip to be determined and the crack advance to be monitored dynamically. The data obtained gives insight into the significance of periodic overloading and underloading as well as what effect interactions, such as crack branching, have on crack growth retardation. The results obtained also enable numerical and analytical codes to be evaluated.

The objective of this article is to introduce and assess a new plate buckling analysis procedure which can be used for quick, approximate analysis of buckling loads in preliminary design. The method is applied to a range of plate edge support condition combinations including many where results are not readily available. The results obtained using the new procedure were compared against theoretical formulae available in the literature and by finite element analyses with good agreement.

This paper presents artificial neural networks (ANN) and wavelet analysis as methods that can assist high resolution of multiple defects in close proximity in components. Without careful attention to analysis, multiple defects can be mis-interpreted as single defects and with the possibility of significantly underestimated sizes. The analysis in this work focussed on A-scan type ultrasonic signal. Amplitudes corresponding to the sizes of two defects as well as the phase shift parameter representing the distance between them were determined. The results obtained demonstrate very good correlation for sizes and distances respectively even in cases involving noisy signal data.

To lighten structures, many metallic components, such as aircraft wings, are being replaced by composite components. To join these components with the rest of the structure, various joining techniques are used. When using multiple bolted joints, bypass vs. bearing loading is developed around each joint. The ratio of bearing to bypass loading is known to affect the level of load at which failure occurs. There have been many models created to predict failure within composites but very little work has been carried out to investigate how well numerical models predict failure within bolted joints subjected to bearing and bypass loading. In addition, few models have been developed that account for the through thickness stresses that are developed underneath the bearing load. This paper compares a range of failure criteria and degradation models utilizing a three-dimensional model and compares how well they predict failure for bearing vs. bypass loading for a supported-pin-loaded joint.

A series of carbon fibre laminate bearing versus bypass load tests were carried out investigating the effect of hole clearance, laminate lay-up, washer contact size and clamping force value. Explanations of the underlying effects that influence the results are discussed. The results compare well with literature but hole clearance was shown to increase joint strength when bypass loads are dominant, which seems counter intuitive, and has not been reported in literature.

A reduced order model is developed for low frequency, undamped, fully coupled structural–acoustic analysis of interior cavities backed by flexible structural systems. The reduced order model is obtained by applying a projection of the coupled system matrices, from a higher dimensional to a lower dimensional subspace, whilst preserving essential properties of the coupled system. The basis vectors for projection are computed efficiently using the Arnoldi algorithm, which generates an orthogonal basis for the Krylov Subspace containing moments of the original system. The key idea of constructing a reduced order model via Krylov Subspaces is to remove the uncontrollable, unobservable and weakly controllable, observable parts without affecting the transfer function of the coupled system. Three computational test cases are analyzed, and the computational gains and the accuracy compared with the direct inversion method in ANSYS. It is shown that the reduced order model decreases the simulation time by at least one order of magnitude, while maintaining the desired accuracy of the state variables under investigation. The method could prove as a valuable tool to analyze complex coupled structural–acoustic systems, and their subsequent optimization or sensitivity analysis, where, in addition to fast analysis, a fine frequency resolution is often required.

The torsional stiffness of automotive structures is one of the major factors that affect vehicle handling, safety, and passenger comfort. Several factors such as material property, geometry, and joining technique contribute to the stiffness of a structure. This paper investigates the effect of adhesive modulus on the torsional stiffness of idealized box beams and plenum chambers for a luxury car. The effect of the adhesive modulus was investigated by considering four different adhesives ranging in modulus from 6MPa to 3GPa. To assess the influence of the apertures, some tests were carried out on box beams containing a single aperture on one side. Spot-welded plenum chambers and beams were also tested to provide a comparison of adhesive performance against conventional methods of joining. The effect of the adhesive modulus and the apertures in the plenum chambers on the results are discussed in the light of the results obtained, as well as the suitability of using adhesive bonding to reduce the vehicle weight.

Locating defects and classifying them by their size was done with an Adaptive Neuro Fuzzy Procedure (ANFIS). Postulated void of three different sizes (1x1 mm, 2x2 mm and 2x1 mm) were introduced in a bar with and without a notch. The size of a defect and its localization in a bar change its natural frequencies. Accordingly, synthetic data was generated with the finite element method. A parametric analysis was carried out. Only one defect was taken into account and the first five natural frequencies were calculated. 495 cases were evaluated. All the input data was classified in three groups. Each one has 165 cases and corresponds to one of the three defects mentioned above. 395 cases were taken randomly and, with this information, the ANN was trained with the backpropagation algorithm. The accuracy of the results was tested with the 100 cases that were left. This procedure was followed in the cases of the plain bar and a bar with a notch. In the next stage of this work, the ANN output was optimized with ANFIS. The accuracy of the localization and classifications of the defects was improved.

A method is presented to demonstrate the use of artificial neural networks (ANNs) in providing additional information regarding defects or flaws when used in conjunction with the ultrasonic A-scan method. ANNs were employed both as pattern classifiers and as function approximators to maximise the amount of data available from the temporal A-scan signal. A steel bar was modelled in 2D using ABAQUS finite element analysis (FEA) software. A single defect was introduced to the bar, modelled as a void, and parametric studies conducted to record data with the defect at various locations. An ultrasonic Lamb wave was introduced at the top of the bar. The longitudinal wave propagated along the length of the bar and was partially reflected by the defect. Multiple cases were simulated, modelling voids between 1mm and 6mm in width in various locations. Mean displacement of all the nodes at the top of the bar was recorded throughout the simulation, and features extracted from this waveform to create the data set for the ANNs. The ANNs were trained with a percentage of the data collected, selected at random, and assessed with the remaining data. The target data for the ANNs were the depth and size of the defect. The case of two separate defects was also investigated. The procedure was carried out in the same manner as for one defect, but in this case the target data for the ANNs were the depth of the first defect and the distance between the defects. A separate ANN was employed as a pattern classifier, to determine if the reflected A-scan signal represented one or two defects. The final system was tested using previously unseen data, and provided very good results both in determining the number of defects and the size and location of the defects, even with data to which noise had been added.

Adhesive bonding is used increasingly by the automotive industry to join structural components of metallic and composite materials. The most common joint configuration is the lap-shear joint, which has been investigated widely and several ideas have been proposed to improve its performance. For example, the introduction of fillets at the overlap ends or tapering of the substrates can reduce the peel stresses at the overlap end. Other approaches include the use of ‘reverse-bent’ substrates and ‘wavy joints’ to reduce peel stresses. This paper compares the stress distribution of the ‘reverse-bent’ and the ‘wavy joint’, with the stresses of the traditional lap-shear joint, using finite element analysis (FEA). A parametric study was carried out showing trends influencing stresses in the adhesive layer. Experimental tests were conducted to evaluate the assumptions used in, and the findings of, the FEA. The joint strength of ‘reverse-bent’ joints was found to be up to 40% higher compared to flat joints using various substrate materials, adhesives and overlap lengths.

This work used the crack compliance method for the determination of residual stresses in beams subjected to prior straining before the introduction of residual stresses through bending. The paper also introduces a support system that allows free movement of specimens during cutting by electric discharge machining. The experimental testing and verification procedure considered factors such as different materials and strain hardening levels. The results obtained provide a quantitative demonstration of the effect of prior strain hardening on residual stress distribution in beams.

Some materials exhibit Bauschinger effect as a consequence of strain hardening. The effect leads to asymmetric tensile and compressive stress-strain behaviour. If the hardening behaviour in either tension or compression is known, combined isotropic/kinematic hardening rules can be used to estimate the hardening behaviour in the other. These rules are, however, only approximate empirical relationships that are derived from the analysis of separate tensile and compressive test results. This article presents a method for the simultaneous derivation of tensile and compressive stress-strain behaviour from bending tests only. The information required is strains at the top and bottom surfaces of beams and moment as load is incrementally applied. The derivation of the method is based on the application of tensile and moment equilibrium conditions. The proposed method is tested on theoretical data obtained from finite-element analysis and as well as on data from actual experimental testing. The agreement between the results obtained is very good.

An inverse artificial neural network (ANN) assessment for locating defects in bars with or without notches is presented in the paper. Postulated void defects of 1mm x 1mm were introduced into bars that were impacted with an impulse step load; the resultant elastic waves propagate impinging on the defects. The resultant transient strain field was analyzed using the finite element method. Transient strain data was collected at nodal points or sensors locations on the boundary of the bars and used to train and assess ANNs. The paper demonstrates quantitatively, the effects of features such as the design of ANN, sensing parameters such as number of data collection points, and the effect of geometric features such as notches in the bars.

A new inverse method has been developed for the simultaneous derivation of tensile and compressive stress strain behaviour from bending tests only. This new procedure can be applied to materials having asymmetric tensile and compressive stress strain behaviour and also materials that have been previously strain hardened (Bauschinger Effect). This paper presents results obtained using the new method and compares them with experimentally obtained tensile and compressive stress strain curves. The agreement of the derived stress strain data in tension and compression is encouraging.

High stress gradients occur at metal-to-ceramic joints due to the different thermal and mechanical properties of the materials. In some cases, the magnitude of the highly localized stresses lead to failure thus compromising the structural integrity of such joints. The study of notched ceramic bars with high stress gradients can assist with the prediction of failure of metal ceramic joints. Experiments and fracture mechanics analysis were performed on notched and un-notched POCO E.D.M 3 graphite and AS800 Silicon Nitride bars with different notch parameters. The twoparameter, multi-axial Weibull statistics method and a brittle fracture criterion based on the average stress over an area approach were used to predict the failure of the bars and the results obtained were compared with experimental results. The brittle failure criterion appears to give much better correlation with experimental results than the multi-axial Weibull statistics approach. The findings also appear to highlight the limitations of the Weibull’s statistics method in cases involving very high stress gradients.

The solutions of damage progression models are known to be highly step-size dependent. This paper develops a new algorithm to predict damage, within a problem at a given load, which is not step size dependant. Two different cases have been studied using the current load incremented solution methods and the new algorithm. The algorithm is shown to produce similar results when compared to the load incremented solution methods, when they utilise a small enough step size to ensure a converged solution. It is therefore concluded that the new failure algorithm can save time by providing confidence that a correctly converged solution has been found without the need to carry out an iterative check on the effect of step size.

The paper explores the use of surface strain measurements to determine residual stresses induced in plastically bent beams under Bauschinger effect using minimal stress–strain data. The hardening behaviour of some materials in tensile and compressive are dissimilar specially under Bauschinger effect. It is therefore ideally necessary to obtain both tensile and compressive stress–strain data in order to determine residual stresses in such materials. The paper shows that the compression stress–strain data can be implicitly determined from tensile and bending stress–strain results by using equilibrium considerations. This removes the need for separate compression tests to be carried out. The agreement of the derived stress–strain data in compression and of residual stress with experimental results is encouraging.

This article presents the work carried out to design and build an apparatus to enable the bearing versus by-pass failure envelope to be determined for a range of composites. The apparatus incorporates a removable rig (consisting of a loading frame, actuators and additional load cell) with a servo-hydraulic testing machine to enable the application of both by-pass and bearing load. The advantages of this system are that independence between the loads can be achieved with minimum cost while enabling the servo-hydraulic machine to be utilized for other test types. Additionally, a variety of specimen geometries, bolt sizes, clamp-up areas, clamp-up pressures, by-pass loads and bearing loads can be applied. Initial tests have shown that the apparatus performs well.

The stress concentration towards the ends of a bonded lap joint depends to some extent on the relative stiffnesses of the adherend and the adhesive used. For a given adherend, the lower the stiffness of the adhesive used in the bondline, the lower the stress concentration, giving rise to potentially higher joint strength. The paper presents the results of a study of the application of two adhesives with different stiffnesses along the overlap length in single lap joints. A stiff adhesive was applied in the middle portion of the overlap, while a low modulus adhesive was applied towards the edges prone to stress concentrations. The results show measurable increase in strength of the bi-adhesive bonded joints compared with those in which single adhesives were used over the full length of the bondline.

Adhesive bonding of joints in car bodies is known to contribute significantly to increased body stiffness. Although analytical techniques are able to give some prediction of this enhancement, the accuracy of the models is limited by the relatively low resolution of the joint details. Numerical models based on finite element analysis have been used to model different joint configurations and their predicted stiffness characteristics have been validated by experimental testing. It is shown that the joint stiffness of coach (T-peel) joints is critically dependent on the presence of fillets and flange bend radii. The application of stiffness data from ‘micro-models’ of joints into the prediction of full body behaviour presents computational problems due to the large number of elements in the FE analysis and the associated processing time. Various substitution element methods have been considered and a novel approach has been developed based on an undercut element concept. This technique enables the designer/modeller to easily include corrections for different joint parameters in the analysis of structural details in full body behaviour without the need for extended computing requirements.

Aerospace and industrial gas turbine engine components such as discs, blades, and propeller shafts for which long fibre reinforced metal matrix composites are being made, operate under complex mechanical and thermal loading conditions. A major aim of functionally graded materials application is to optimise component response through appropriately tailored microstructures. This paper explores the influences of property gradation, centrifugal body force loading, and thermal loading on stresses in rotating discs. The discs were modelled as non-homogeneous orthotropic materials such as those obtained through non-uniform reinforcement of a metal matrix by long fibres. The results show how different temperature change distribution patterns and property gradation types correlate with hoop stresses developed in the disc.

Functionally graded materials are attractive because of the additional possibilities they offer for optimising the design of components in terms of material usage and performance. This paper explores, in part, the potential benefits of using fibre-reinforced, functionally graded materials for rotating hollow and solid disks. Several forms of gradation with the same nominal volume fraction of reinforcement were considered. This established a basis for comparison of results obtained for the different cases. The functionally graded material was modelled as a non-homogeneous orthotropic material. The finite-element method and direct numerical integration of the governing differential equations were used to predict stress and deformation distribution in the disks.

Thermal residual stresses in Ti6Al4V alloy reinforced with silicon carbide (SiC) and sapphire alumina (Al2O3) fibres are estimated based on an elastic-viscoplastic micromechanics analysis. Effects of fibre volume fraction and different manufacturing procedures are considered and comparisons made with published experimental results for the SiC fibre composite. Stress components in the Ti-6-4/Al2O3 are generally less than half the corresponding values in the Ti-6-4/SiC composite.

Structural defects such as fibre breakage, fibre cracking and matrix cracking were found in experimentally consolidated composites near the interface region. Micromechanical and finite element analyses were developed to investigate the causes for such defects. Large heterogeneous contact pressures at the early stages of fabrication can lead to decohesion or cracking of fibre coatings. Excessive stretching and bending during consolidation generate transverse cracking and/or breakage of fibres. The limits of such deformation were predicted and applied for SiC fibre-reinforced titanium composite systems. A high level of residual hoop tension in the matrix can be introduced in the interface region during cooling. Experimentally observed residual voids in the interface region were found to enhance the matrix tensile hoop stress and create tension around the outer region of the fibre, raising the propensity towards cracking around the void. Theoretical results are discussed in line with the experimental conditions.

The paper presents results of numerical micromechanics fatigue characterization of Ti-6Al-4V/SiC composites having different fibre array packs and volume fractions. Multiaxial fatigue criteria which take the residual stress effects into consideration are applied. The results obtained show that optimum fibre volume fraction for reinforcement is dependent on required design load.