The coefficient of restitution (COR) is one of the most important standards in all bat-and-ball sports to study the ball suitability and bouncing characteristics. Currently, a demand exists for an improved analytical model for computing the COR, particularly when a new material is introduced. A viscoelastic ball known as a sliotar is taken from the Irish bat-and-ball sport of hurling and used in this paper as a case study. For the theoretical approach, a modified Maxwell's viscoelastic model was used to derive an analytical formula to predict the COR. The developed formula confirms that the damping ratio and the dependency of the COR on the impact speed are the most dominant factors. A 3-dimensional finite element (FE) model was developed to simulate the sliotar impact test to estimate the COR, and to provide a simple tool for further studies on the sliotar. A high-speed camera was used to film the impact event to validate the models' behavior. The close correlation of 9% and 5% between the experimental and the developed analytical and FE models, respectively, indicates that the developed models can successfully identify the COR of the ball in the game of hurling and similar bat-and-ball sports.
In this paper, the process of training an artificial neural network (ANN) on predicting the hysteresis of a viscoelastic ball and ash wood bat colliding system is discussed. To study how the material properties and the impact speed affect the hysteresis phenomenon, many experiments were conducted for colliding three types of viscoelastic balls known as sliotars at two different speeds. The aim of the study is to innovate a neural network model to predict the hysteresis phenomenon of the collision of viscoelastic materials. The model accurately captured the input data and was able to produce data sets out of the input ranges. The results show that the ANN model predicted the impact hysteresis accurately with <1% error.
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