Prediction of under pickling defects on steel strip surface

An extremely important part of the finishing line is the pickling process, in which oxides formed during the hot rolling stage are removed from the surface of the steel sheets. The efficiency of the pickling process is mainly dependent on the nature of the oxide present at the surface of the steel, but, also, on process parameters such as bath composition and time duration are relevant. When acid concentration, solution temperatures and line speed are not properly balanced, in fact, sheet defects like under pickling or over pickling may happen and their occurrence does have a very serious effect on cold-reduction performance and surface appearance of the finished product. Furthermore, product damage from handling or improper equipment adjustment can render the steel unsuitable for further processing. This is the reason why it is important that process significant parameters are controlled and maintained as accurately as possible in order to avoid these undesired phenomena. In the present work, a control algorithm, composed by two different modules, i.e. decision tree and rectangular Basis Function Network, has been implemented to aim of predicting pickling defects and suggesting the optimal speed or the admissible speed range of the steel strip in the process line. In this way the most suitable line speed value can be set in an automatic way or by the technical personnel.Comment: 9 Pag

Optimization of data resampling through GA for the classification of imbalanced datasets

Classification of imbalanced datasets is a critical problem in numerous contexts. In these applications, standard methods are not able to satisfactorily detect rare patterns due to multiple factors that bias the classifiers toward the frequent class. This paper overview a novel family of methods for the resampling of an imbalanced dataset in order to maximize the performance of arbitrary data-driven classifiers. The presented approaches exploit genetic algorithms (GA) for the optimization of the data selection process according to a set of criteria that assess each candidate sample suitability. A comparison among the presented techniques on a set of industrial and literature datasets put into evidence the validity of this family of approaches, which is able not only to improve the performance of a standard classifier but also to determine the optimal resampling rate automatically. Future activities for the improvement of the proposed approach will include the development of new criteria for the assessment of sample suitability