A neural network model for prediction of strength loss in threads during high speed industrial sewing

In this paper artificial neural network (ANN) model has been designed to predict the strength loss in threads during high speed industrial sewing. Four different types of threads (Mercerized cotton, polyester staple spun, polyester-cotton core spun and polyester-polyester core spun) were taken for the study. The other input parameters include thread linear density, fabric area density, number of fabric layers, stitch density and needle size. In order to reduce the dependency of the results on a specific partition of the data into training and testing sets, a four-way cross validation tests were performed, i.e. total data was divided into training and testing set in four different ways. The predicted tenacity loss was correlated to the experimental tenacity loss and correlation coefficient between the actual and predicted tenacity loss obtained. It was observed that the neural network system is able to predict the tenacity loss of threads after sewing with good correlation and less average error. The relative contribution of each parameter to the overall prediction of the tenacity loss was studied by carrying out the sensitivity analysis of the test data set. The results of sensitivity analysis show that thread type is the most important input parameter followed by thread linear density, number of fabric layers, fabric area density, needle size and the stitch density.