TOPMOST STEEL PRODUCTION DESIGN BASED ON THROUGH PROCESS MODELLING WITH ARTIFICIAL NEURAL NETWORKS

摘要

Application of artificial neural networks for modeling of a complete process path in a steel production - from the scrap steel to the material properties of semi products - is presented. The described approach is introduced as an alternative to physics based through process modeling, with the advantage of lower complexity of the software and much lower computing times for calculating the influence of a specific settings of the process parameters. This new approach can be beneficially used in designing the production process. This is clearly demonstrated by estimating the influence of 34 alloying elements and process parameters of 6 process steps on 5 final mechanical properties of spring steel (elongation, tensile strength, yield stress, hardness after rolling and necking), based on 1879 recorded data sets from the production line in Store Steel company. The ANN used is of a multilayer feedforward type with sigmoid activation function and supervised learning. An important feature of this approach is its dependence on accurate and sufficient data, acquired from the modeled process. Therefore, special care must be devoted to validation of the obtained model and error estimation. The reliability and other characteristics of the available data can vary to a great extent in real industrial practice, therefore analysis of the models is a highly customized task that has to be performed on a case to case basis. A flexible and easily extensible software base has been developed in the scope of the described work in order to adequately support research, development and practical application of this kind of models.