The virtual manufacturing model of the worsted yarn based on artificial neural networks and grey theory

Grey incidence analysis has been primarily studied in order to efficiently select the input variables of artificial neural networks (ANNs). The analysis of the processed data indicates that the weightiness sequence of all the variables influencing on the yarn quality and spinning performance could be ranked according to the grey incidence matrix. The comparison of the performance of ANNs model using grey superior analysis (GS), subjective and empirical approach (SE), and multi-linear regress method (MLR) shows that the model using the input variables selected by GS is superior to that by SE and MLR. At the same time, the virtual manufacturing model (VMM) of the worsted yarn has been used to simulate the spinning on the basis of ANNs and GS. In addition, through adjusting the first superior factors in the VMM, the yarn quality could be controlled and obviously improved.