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- Title
人工智能技术驱动的纺纱质量预测研究进展.
- Authors
赵薇玲; 章军辉; 陈明亮; 李庆; 陈大鹏
- Abstract
The yarn production process is a complex multi-step process, and the yarn quality is affected by raw material properties, process parameters and equipment parameters. The spinning mills attempt to predict and control the yarn quality in advance through the spinning data, so they can adjust the production process parameters in time according to the individual needs of customers, and achieve the goal of reducing raw material waste, improving yarn quality and even reducing costs. With the development of artificial intelligence technologies such as big data and intelligent modeling, artificial intelligence technology has been gradually applied in the spinning industry. Aiming at the problem of yarn quality prediction, researchers have conducted a lot of research on platform framework, algorithms and models, accelerating the application of artificial intelligence technology in the spinning industry. Most of the statistical correlation methods such as simple mathematical models and multiple linear regression have certain idealized assumptions, which are strongly dependent on production experience and involve obvious subjective factors. At present, researchers are committed to the application of artificial intelligence methods in the field of spinning, and propose a variety of spinning quality prediction models based on neural networks. Compared with the traditional method, the self-learning and adaptive ability of neural networks can quickly learn the nonlinear relationship between fiber parameters, process and equipment parameters and yarn quality indicators. The model has high prediction accuracy and generalization ability. Aiming at the parameter optimization problem of neural networks, the related research combines the improved swarm intelligence algorithm to realize model parameter tuning, accelerate model convergence and improve prediction accuracy. Aiming at the problem of spinning small sample modeling, related research uses support vector machine combined with swarm intelligence algorithm to propose a small sample modeling method with strong adaptability. Related research also applies the swarm intelligence algorithm to the inversion of yarn raw material parameters, and obtains the optimal solution set by converting it into a multi-objective optimization problem. There are also some researches devoted to the design of computing platform framework for yarn quality prediction modeling, which mainly uses Hadoop technology to provide reliable and efficient underlying technical support for yarn quality prediction process. In addition, the fuzzy method is another important method in the research of yarn quality prediction. The related research has gone through simple fuzzy logic to the combination of swarm intelligence algorithm, neural network and fuzzy system, which preliminarily reflects the fusion idea of the knowledge-driven method and the data-driven method, and provides more ideas for the research of the spinning field. At present, although artificial intelligence technology has accumulated a lot of achievements in the field of yarn quality prediction, there are still some common problems in the existing research, which are mainly reflected in the fact that the model does not have the ability to adapt to massive data, and that the research lacks sequential situation prediction problems and intelligent decision-making. Therefore, researchers still need to carry out in-depth research on these problems and continue to tap the application potential of artificial intelligence technology in the field of yarn quality prediction.
- Publication
Journal of Silk, 2023, Vol 60, Issue 4, p61
- ISSN
1001-7003
- Publication type
Article
- DOI
10.3969/j.issn.1001-7003.2023.04.009