60 resultados para MUTUAL FRICTION

em Deakin Research Online - Australia


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The skin friction coefficient on the surface of a rotating yarn package affects the power required to drive the package. This paper examines the relationship between the skin friction coefficient on the package surface and its diameter and rotating speed, based on the fundamentals of aerodynamics and the experimental results of power consumption. Skin friction coefficients on the surfaces of an airplane, car top, and yarn package are discussed. The results indicate that the skin friction coefficient on the package surface without hairiness depends on the package diameter and spindle speed only. The skin friction coefficient on the yarn package surface is about three times that on the top surface of a car, and is about twenty times that on an airplane surface. The power consumed to overcome skin friction drag is more than that consumed to drive the spindle if the spindle speed is very slow. However, the situation reverses when the spindle speed is fast.

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A compression test with rotating dies was employed to estimate the friction factor between aluminum samples and steel tooling during large plastic deformations. A cylindrical workpiece was compressed under dry and cold conditions. The magnitudes of torque and normal force were measured and
the average friction factor was calculated using the Coulomb friction law. It was found that under certain conditions the friction increased with increasing angular velocity of the die. This finding led to the conclusion that the choice of die rotation speed is important in interpreting the results from the twist compression test.

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The purpose of this article is to consider a peculiar type of will known in equity as a mutual will. The major focus is a consideration of the interplay of the legal effect of mutual wills and the practical use of such wills, It is hoped that this discussion will assist legal practitioners in identifying those clients who may benefit from executing mutual wills, identifying those clients who might intend to execute mutual wills, advising clients as to the pros and cons of mutual wills and the benefits of accompanying such wills with a deed or contract.

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To quantify the frictional behaviour in sheet forming operations, several laboratory experiments which simulate the real forming conditions are performed. The Bending Under Tension Test is one such experiment which is often used to represent the frictional flow of sheet material around a die or a punch radius. Different mathematical representations are used to determine the coefficient of friction in the Bending Under Tension Test. In general the change in the strip thickness in passing over the die radius is neglected and the radius of curvature to thickness ratio is assumed to be constant in these equations. However, the effect of roller radius, sheet thickness and the surface pressure are also omitted in some of these equations. This work quantitatively determined the effect of roller radius and the tooling pressure on the coefficient of friction. The Bending Under Tension Test was performed using rollers with different radii and also lubricants with different properties. The tool radii were found to have a direct influence in the contact pressure. The effect of roller radius on friction was considerable and it was observed that there is a clear relationship between the contact pressure and the coefficient of friction.

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This paper formulates the problem of learning Bayesian network structures from data as determining the structure that best approximates the probability distribution indicated by the data. A new metric, Penalized Mutual Information metric, is proposed, and a evolutionary algorithm is designed to search for the best structure among alternatives. The experimental results show that this approach is reliable and promising.

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We address the problem of adaptive blind source separation (BSS) from instantaneous multi-input multi-output (MIMO) channels. It is known that the constant modulus (CM) criterion can be used to extract unknown source signals. However, the existing CM based algorithms normally extract the source signals in a serial manner. Consequently, the accuracy in extracting each source signal, except for the first one, depends on the accuracy of previous source extraction. This estimation error propagation (accumulation) causes severe performance degradation. In this paper, we propose a new adaptive separation algorithm that can separate all source signals simultaneously by directly updating the separation matrix. The superior performance of the new algorithm is demonstrated by simulation examples

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This paper tells a story of synergism of two cutting edge technologies — agents and data mining. By integrating these two technologies, the power for each of them is enhanced. Integrating agents into data mining systems, or constructing data mining systems from agent perspectives, the flexibility of data mining systems can be greatly improved. New data mining techniques can add to the systems dynamically in the form of agents, while the out-of-date ones can also be deleted from systems at run-time. Equipping agents with data mining capabilities, the agents are much smarter and more adaptable. In this way, the performance of these agent systems can be improved. A new way to integrate these two techniques –ontology-based integration is also discussed. Case studies will be given to demonstrate such mutual enhancement.