Dr Neil Fellows

The crashworthiness behavior of horsetail-inspired sandwich tubes was analyzed in this study. Multilayer perceptron (MLP) algorithms with the Levenberg-Marquardt training algorithm (LMA) were used to predict force-displacement curve and optimize the geometrical parameters according to minimum peak crushing force and specific energy absorption. Based on the non-dominated sorting genetic algorithm II (NSGA-II) optimization results, the specimen with four core tubes and a thickness of 1 mm, and a height of 92 mm has the optimal crashworthiness performance. Finally, the optimal specimen is fabricated and the results of the numerical and MLP methods are validated versus experimental approach.

This article investigates the energy absorption and failure behavior of thermoplastic composite sandwich panels made entirely of polypropylene (PP) and pin-reinforced core under quasi-static compressive loading. The pins are manufactured by thermoforming and assembled with face sheets. The specimens were subjected to flatwise compressive loading to examine energy absorption capabilities. Moreover, the finite element method (FEM) is used to analyze core sandwich panels reinforced with cubic, cylindrical, beam, and cross-beam pins. Furthermore, a closed-form analytical model is adopted and developed to predict the critical load of these structures. The performed experiments were utilized to validate the damage mechanisms and critical displacements of the simulations and the analytically calculated maximum collapse loads. The results demonstrate that the predictions accurately capture both the critical failure load and failure mechanisms. Since the numerical results have a reasonable correlation with the experimental results and their output difference is

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 exposure of pistons to extreme mechanical and thermal loads in modern combustion engines has necessitated the use of efficient and detailed analysis methods to facilitate their design. The finite element analysis has become a standard design optimisation tool for this purpose. In literature two different approaches have been suggested for reducing the geometry of the cylinder and crank slider mechanism, to idealise piston finite element analysis load models,whilst trying to maintain realistic boundaries to obtain accurate results. The most widely used geometry is the combination of piston and gudgeon pin while the second geometry includes some portion of the connecting rod’s small end and cylinder in addition to the piston and gudgeon pin.No clear analyses have been made in literature about the relative effectiveness of the two approaches in terms of model accuracy. In this work both approaches have been carried out and analysed with respect to a racing piston. The results suggest that the latter approach is more representative of the load conditions that the piston is subjected to in reality.

Autonomous vehicles have been shown to increase safety for drivers, passengers and pedestrians and can also be used to maximize traffic flow, thereby reducing emissions and congestion. At the same time, governments around the world are promoting the usage of Battery Electric Vehicles (BEVs) to reduce and control the emissions of CO2. This has made the development of autonomous vehicles and electric vehicles a very active research area and has prompted a significant amount of government funding. This paper presents the detailed design of a low-cost platform for the development of an autonomous electric vehicle. In particular, it focuses on the design of the electrical architecture and the control strategy, tailored around the usage of affordable sensors and actuators. The specifications of the components are extensively discussed in relation to the performance target. The aim is to provide a comprehensive guide for the development of the remotely controlled platform, in order to lower the entry barrier for the development of autonomous electric vehicles.

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. 

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

Adhesively bonded lap shear joints have been investigated widely and several ideas have been proposed for improving joint strength by reducing bondline stress concentrations. These include application of adhesive fillets at the overlap ends and use of adhesive with graded properties in the overlap area. Another, less common, approach is to deform the substrates in the overlap area in order to obtain a more desirable bondline stress distribution. Previous work carried out by the authors on a number of different substrate materials indicated that a reverse-bent joint geometry is useful for increasing joint strength. Results from static stress analysis and experimental testing demonstrated that significant improvements could be achieved. This paper presents results of further work carried out to assess the fatigue performance of reverse-bent joints. Substrates with different yield and plastic deformation characteristics were used and the effects of different overlap lengths on strength were examined. The results of this research show that the improvements obtained under static tests conditions translate to even higher benefits in fatigue. The paper also explains the failure mechanism of the joints under fatigue loading.

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.

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.