Predicting the dynamic cohesion in drafted slivers at draw frame using artificial neural networks

The cohesion among the fibers in a sliver assembly plays an important role in determining the material behavior during further drafting operations. a proper control exerted on fiber to fiber friction can help to eliminate the drafting problems during the spinning process and positively influence yarn quality. the present research work aims to explain the influence on various draw frame parameters on the sliver cohesion. cotton,polyester and cotton polyester blend were selected and processed using different draw frame variables. the dynamic cohesion force was measured using rothschild cohesion meter. different materials showed different level of cohesion,whereas,draw frame variables also influenced the cohesion forces in drafted slivers. an artificial neural network (ann) model was developed to predict the sliver cohesion by using draw frame parameters as input to the ann. the results showed that cohesion force in drafted slivers can be successfully predicted with the help of anns.