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.

Metal coating nowadays is very essential in heavy industry and many other applications, however, a coating system is designed and built to obtain pyrolytic Chrome-Oxide Cr2O3, so oxygen is distributed through the coating in order to enhance its properties depending on metal-organic compounds (MOC). A very large number of experiments have been performed to study the effect of oxidant comparing with inert atmosphere. A chemical vapor deposition method for preparing chromium oxide Cr2O3 coatings from bis-arene chromium compounds has been performed, followed by studying the effect of oxidant substances concentration on the kinetics of growth of coatings. The main finding is that coatings exhibit excellent adhesion, high microhardness, and wear resistance. The coating process is characterized by high adaptability and relatively low cost.

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