Prediction of under pickling defects on steel strip surface

TL;DR

This paper presents a control algorithm combining decision trees and Basis Function Networks to predict under pickling defects on steel surfaces and optimize process line speed for improved quality.

Contribution

It introduces a novel hybrid control algorithm for predicting pickling defects and determining optimal line speeds in steel processing.

Findings

Effective prediction of under pickling defects achieved

Automated speed adjustment improves process consistency

Potential reduction in surface defects and processing costs

Abstract

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.