Application of a neural network for an improved control of the metallurgical process

Abstract

The factors of influence involved in many metallurgical problems are featured by a non-linear relation, so that their control is not an easy task. On the other hand, the quality of the metallurgical aspects of the product depends greatly on the knowledge of the relation that can allow a successful management of the metallurgical process. The technological control of the process can take advantage from the application of non-linear numerical methods that describe and simulate some not easy-understandable behaviours of the metallurgical systems. The neural network can be applied to build an automatic procedure for the definition of the productive parameters to obtain the desired results. Moreover, the neural networks find not only reliable relation for the forecasting task but also to develope a speedy and reliable classification of the different production cases to be treated. The neural network model here shown is devoted to implement both these issues and has been validated on the definition of the effect of the electromagnetic stirring on the solidification structure of a continuous casting machine, but it can be adapted to treat also other metallurgical processes in the foundry operations and so on.