Study On The Cooperative Modelling And Intelligent Optimization Of Carbon Fiber Spinning Process

Caron fiber is a new engineering material, it has ideal engineering characteristics such as high strength and high modulus. Owning to its superiority, it has been widely used in all works of life and influence national military, industry and economic deeply. Since carbon fiber production starts late in China, there is a large gap on carbon fiber performance between our motherland and advanced countries, in that case, it is significative to focus our attention on the optimization of carbon fiber manufacture process. The production line of carbon fiber is long, it contains several key sub-processes, each sub-process has its own control parameters, and these parameters couples each other. So complex production situation and so many factors make us considering that, introduce collaborative mechanism and biological mechanism into our research, adopt artificial intelligence algorithms to modeling and optimize the spinning process of carbon fiber, give out some reference and guides to real-time monitoring the carbon fiber production and improve the performance of carbon fiber product. The specific contributions of this paper summarized as follows:(1)Take the six steps drawing process of carbon fiber production as an example, based on existing experiments data, we investigate the relationship between representative production parameters and product properties indices, built a mathematical model of the carbon fiber drawing process; design a synergistic immune clonal selection algorithm which includes synergy mechanism, clonal selection and nonuniformty mutation, this algorithm can solve the multi-objective optimization problem, give out a six steps drawing ratio distribution scheme. This optimal result can provide reference to the actual production of carbon fiber, and the algorithm also can be applied to similar multi-objective optimization problem. (2)Take the pre-oxidation process of polyacrylonitrile carbon fiber an a example, set up a immune genetic neural network model based on father keeping scheme, we can estimate the performance of carbon fiber during production process by this model. Based on experiments data, we compare our prediction model to existing model, results show that, our model has better prediction precision, and convergent more quickly, it is a reliable performance prediction model which can provide guidance and reference for pre-oxidation process of carbon fiber better.(3)In this paper, we put forward a genetic-improved particle swarm optimization based neural network (GA-IPSO-RNN) bi-directional optimization model for carbon fiber spinning process. First of all, we use nearest neighbor clustering algorithm to determine the hidden layer nodes of neural network; secondly, we propose a genetic-improved particle swarm optimization algorithm to tune all the parameters of the neural network, which means activate function and weights between layers. For one direction, we can predict properties of carbon fiber by this model; for another direction, we can provide a design tool of new type carbon fiber. Combining this novel GA-IPSO algorithm into neural network plays a role to develop artificial intelligence; base on the bi-directional optimization model of carbon fiber spinning process, we can attempt to realize online monitor and control the manufacture line, real-time predict quality of carbon fiber, adjust the produce parameters in time, avoid to cause serious economical loss; similarly, this model can offer one or several production schemes which approximate meet our expect product properties, provide some guidance and help to actual production, prevent into mass production directly and lead to waste of time and money.At the end, we summary the full text of the research, discuss the deficiency of our investigate results, and propose future research direction.