Yarn Quality Prediction and Process Parameters Optimization Based on Genetic Algorithm and Neural Network

Abstract

Quality prediction is an important means of the quality management in modern spinning production. This paper proposed a yarn quality prediction model based on Genetic Algorithm and back propagation neural network to predict the yarn quality and optimize the process parameters. The main identification model parameters were optimized by using genetic algorithm, and the prediction performance of the model has been compared against that of the BP neural network model. The effectiveness and availability of the proposed model are verified with the use of actual production data.