Currently, the manufacture of composite structures often requires material removal operations using a cutting tool. Indeed, since biocomposites are generally materials that do not conduct electricity, electro-erosion cannot be utilized. As a result, the processes that can be used are limited to conventional machining, called chip removal machining, such as drilling. Delamination factors are widely recognized for controlling the damaged area (delamination) induced by drilling in industry. As discussed in the literature, several approaches are available to evaluate and quantify the delamination surrounding a hole. In this context, the objective of this study is to compare the three Fd evaluation methods that have been most frequently used in previous investigations. To this end, three rotational and feed speeds and three BSD tool diameters were selected (L27) for drilling 155 g/m2 density jute fabric reinforced polyester biocomposites. The response surface methodology (RSM) and artificial neural networks (ANNs) were applied to validate the results obtained experimentally as well as to predict the behavior of the structure depending on the cutting conditions.
Currently, the manufacture of composite structures often requires material removal operations using a cutting tool. Indeed, since biocomposites are generally materials that do not conduct electricity, electro-erosion cannot be utilized. As a result, the processes that can be used are limited to conventional machining, called chip removal machining, such as drilling.Delamination factors are widely recognized for controlling the damaged area (delamination) induced by drilling in industry. As discussed in the literature, several approaches are available to evaluate and quantify the delamination surrounding a hole. In this context, the objective of this study is to compare the three Fd evaluation methods that have been most frequently used in previous investigations. To this end, three rotational and feed speeds and three BSD tool diameters were selected (L27) for drilling 155 g/m 2 density jute fabric reinforced polyester biocomposites. The response surface methodology (RSM) and artificial neural networks (ANNs) were applied to validate the results obtained experimentally as well as to predict the behavior of the structure depending on the cutting conditions.
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