FORECASTING QUALITY INDICATORS OF POLYMER FILMS USING A RECURRENT NEURAL NETWORK
The paper describes using recurrent neural networks to the problem of forecasting the quality of polymer films. There is an experimental determination of the neural network architecture and its main hyperparameters carried out to achieve the minimum error. The mathware interacts with the database of the polymeric film production characteristics. The database forms an array of data for analysis, including the characteristics of the films produced and the technological parameters of the production process at a given time interval. The forecasting algorithm is implemented as a module of software package for big data mining and control of the polymeric film production and tested by using the experimental data from extruder and calender production line.