FDY (fully drawn yarn) spinning technology for optimizing multi-model method based on mixture Gaussian weighting function

Abstract

The invention discloses an FDY (fully drawn yarn) spinning technology for optimizing a multi-model method based on a mixture Gaussian weighting function. The relation models between the breaking strength and orientation degree of a FDY as well as between the crystalline degree and the fiber elongation rate of the FDY are firstly established, meanwhile the relation function between the temperatures and speeds of GR1 and GR2 and the orientation degree and the crystalline degree of the FDY is established, then a database is established, finally the temperatures and speeds of the GR1 and GR2 are calculated according to the relation function by selecting the breaking strength, and the spinning technology is adjusted. The establishment of the relation model comprises the following steps: 1) determining the number of local models; 2) determining the structures of the local models; 3) determining the mixture Gaussian weighting function; 4) estimating unknown parameters in the local models and the mixture Gaussian weighting function; and 5) obtaining the relation model. The model established in the invention has the characteristics of high accuracy and high reliability, and the spinning technology in the invention is beneficial for preparing fibers with excellent mechanical property.

Description

technical field [0001] The invention belongs to the field of FDY polyester filament spinning technology, and relates to a FDY polyester filament spinning technology optimized based on a multi-model method of mixed Gaussian weight functions. Background technique [0002] The existing FDY polyester filament model can establish a model of fiber structure and fiber breaking strength, but it cannot directly reflect the change of fiber properties through process adjustment. Based on actual needs, establish the quantitative relationship between the structural properties of FDY polyester filament spinning process, that is, the relationship model between fiber breaking strength and orientation degree, crystallinity, and fiber elongation. While predicting the fiber breaking strength through known process parameters , and the model can also be reversely used in the process optimization of the normal production process, laying the foundation for the development of new technologies and n...

AI Technical Summary

Problems solved by technology

However, when the distance between adjacent operating points is large, the traditional Gaussian weight function is not enough to express the transition process data

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