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 …
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…
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 the miniaturised hydraulic three way control valve technology.
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.
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.
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 …
This paper presents artificialneuralnetworks (ANN) and wavelet analysis as methods that can assist highresolutionof 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
The automotive and aerospace industries are keen to reduce their environmental impact and so have looked to move to lightweight materials. This creates issues in terms of joining, using and disposing of dissimilar materials. Oxford Brookes has therefore worked with national and multi-national companies in the adhesive, materials, automotive and aerospace industries to try to solve these problems. This has resulted in high quality research publications, innovative test equipment, improved numerical methods, novel designs, design guidelines, manufacturing procedures, British Standards, patents, commercial products and further funding. The impact of the work has global safety, environmental and economic benefits with multi-national aerospace and automotive companies implementing the results in current developments.
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 materia…
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 …