Steel Quality Monitoring Using Data-Driven Approaches: ArcelorMittal Case Study

Authors

Laib M., Aggoune R., Crespo R., Hubsch P.

Reference

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13377 LNCS, pp. 63-76, 2022

Description

Studying manufacturing production process via data-driven approaches needs the collection of all possible parameters that control and influence the quality of the final product. The recorded features usually come from different steps of the manufacturing process. In many cases, recorded data contains a high number of features and is collected from several stages in the production process, which makes the prediction of product quality more difficult. The paper presents a new data-driven approach to deal with such kind of issues. The proposed approach helps not only in predicting the quality, but also in finding to which stage of the production process the quality is most related. The paper proposes a challenging case study from ArcelorMittal steel industry in Luxembourg.

Link

doi:10.1007/978-3-031-10536-4_5