Structural properties and diameter prediction of fine denier polyester fiber based on machine learning algorithms

Abstract

Fine denier polyester fibers have attracted attention in electronic information industry owing to their unique structural properties and ultra fine diameters. Since there is a close connection between melt-spinning process parameters and their structural properties and diameters, the influence of process parameters on fiber structural properties and diameters is an issue worth further exploration. This study aims to construct prediction models based on the Gaussian process regression (GPR), radial basis function neural network (RBFNN) and extreme learning machine (ELM) algorithms to predict the structural properties and diameter of fine denier polyester fibers. For this purpose, firstly, fine denier polyester fibers with different structural properties (i.e., orientation degree and crystallinity) and diameters are produced by changing the key melt-spinning parameters, namely spinning temperature, take-up speed and metering pump speed. Then GPR, RBFNN and ELM methods are applied to predict the structural properties and diameter of fine denier polyester fibers based on the experimental data. Lastly, different predictive performance indicators, namely mean square error (MSE) and mean absolute error (MAE) are imported to evaluate the above three methods. The predicted results which the MSE values of GPR, RBFNN and ELM models are respectively 0.461, 0.358, 0.136, and the MAE values of GPR, RBFNN and ELM models are respectively 0.625, 0.564, 0.320 show that ELM model provided superior performance over the GPR and RBFNN models in predicting the structural properties and diameter of fine denier polyester fibers.