Dr Anand Thite

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 lifetime 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 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.

This paper investigates mathematical modelling and manufacturing of polymer composite Belleville springs, and their potential application. The original expression for load carrying capacity developed for metal springs is refined by considering the variation of elastic modulus and Poisson’s ratio of laminates in polar coordinates. A novel series spring stacking arrangement is proposed to achieve complex stiffness variation by progressive action. The experimental results show consistent effect of number of plies on the spring rate and compare well with the theoretical predictions. Although handmade, the variations in load carrying capacity is very small (~10%) confirming manufacturing viability. It is shown that a smooth, variable spring rate curve can be produced by reducing slip-stick frictional forces with the use of spacers within the spring stacks. In one application, the composite springs are shown to offer significant vehicle dynamic performance improvement through reduction of the tyre contact patch force variation and vehicle body acceleration. 

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. 

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.

Energy losses are of great significance to the automotive and motorsports industries. Many of these losses are incurred during power transmission through the gearbox. There has been considerable research in this area, however, generally gear losses are not calculated at part load condition, nor are so called dry sump systems considered outside of motor racing. The method developed by Anderson & Loewenthal which considers efficiency over part-load conditions is used here to calculate geartrain losses with varying speed, load and temperature conditions in a spur gear type gearbox for motorsport application. Both oil bath (wet sump) and oil jet (dry sump) systems of lubrications are considered. The Changenet method is used to calculate the churning losses in the typical oil bath lubrication system. Seventeen different mineral and synthetic oils were evaluated. At 75kW engine output, 1200W were lost in the dry sump gearbox whereas 1320W were lost in the wet-sump gearbox – in 1st gear at 9000rpm engine speed. The oils studied showed a spread of total drive efficiency of 97.8 to 99% in the most extreme temperature case. Observation of how efficiency and film thickness relate to operating temperatures it is clear that tight temperature control is critical to obtain the potential benefits available from oil optimisation. The dry sump gearbox is predicted to be more efficient only above 5000rpm engine speed.

The aim of this study is to measure and quantify perceived intensity of discomfort due to vibration in a vehicle in-situ considering complete vehicle dynamic behaviour. The shaker table based discomfort curves or the road test results may not accurately and universally indicate the true level of human discomfort in a vehicle. A new experimental method, using a seated human in a car on the four-post rig simulator, is proposed to quantify discomfort. The intensity of perception to vibration decreased with decreasing input and increasing frequency; the rate of change is different from the published literature; the difference is large for angular modes of inputs.  Vehicle dynamic response is used to inform and analyse the results. The repeatability of the method and the fact that they are in-situ measurements may eventually help reduce reliance on the road tests. Furthermore, discomfort curves obtained, subsequently, can be used in predictive models.

In the current environment of increased emphasis on sustainable transport, there is manifold increase in the use of bicycles for urban transport. One concern which might restrict the use is the ride comfort and fatigue. There has been limited research in addressing the difficulty in bicycle ride comfort quantification. The current study aims to develop a methodology to quantify bicycle discomfort so that performance of bicycles constructed from bamboo and aluminium alloy can be compared. Experimentally obtained frequency response functions are used to establish a relation between the road input and the seat and rider response. A bicycle track input profile based on standard road profiles is created so as to estimate the acceleration responses. The whole-body-vibration frequency weighting is applied to quantify the perception of vibration intensity so that eventual discomfort ranking can be obtained. The measured frequency response functions provide an insight into the effect of frame dynamics on the overall resonant behaviour of the bicycles. The beneficial effect of frame compliance and damping on lower modes of vibration is very clear in the case of bamboo frame, in turn affecting seat and rider response. In the bamboo frame, because of multiple resonances, the frequency response of the handlebar is smaller at higher frequencies suggesting effective isolation. Further improvements may have come from the joints made from natural composites. Overall, based on the comparative analysis and the methodology developed, bamboo frame shows significant improvement in ride comfort performance compared with the aluminium frame.

In performing transfer path analysis of structure-borne sound transmission, the operational forces at the excitation points and/or at the connections within the structure-borne paths are required. These forces can be obtained by using inverse techniques but the measured data used will contain some unknown errors. Therefore the reconstructed forces may include large errors due to the inversion of an ill-conditioned matrix of these measured data. In this study, Tikhonov regularization is used in order to improve the conditioning of the matrix inversion. Several methods are available to select the optimal regularization parameter. The purpose of this paper is to compare the performance of the ordinary and generalized cross validation methods and the L-curve criterion. Simulations are carried out, representing measurements on a rectangular plate, for different noise levels in measured data. Also, the robustness of the conclusions is investigated by varying the shape of the plates, the force positions, and the noise levels included in the measured data. The L-curve method is found to perform better than OCV or GCV, particularly for high noise levels in the operational responses, but less well when these noise levels are low. It is therefore found to be less susceptible to producing large reconstruction errors but it tends to over-regularize the solution in the presence of low noise, leading to under-estimates of the forces. In practice, measurements of operational responses may be susceptible to noise contamination which suggests that the L-curve method is likely to be the most appropriate method in practical situations. Nevertheless, it is important to obtain good estimates of the likely noise in the signals before determining the most appropriate regularization technique. Ordinary cross validation method is generally found to have a better performance than generalized cross validation method if the matrix condition numbers are high. Since the need for regularization is greater with high condition numbers, it is consequently found that the ordinary cross validation method gives more reliable results overall than the generalized cross validation method.