Abstract
Among the undesirable quality incidents in the cold rolling process of strip products, strip snap could result in yield loss and reduced work speed. Therefore, it is necessary to reveal the factors influencing the occurrence of this failure for quality improvement. In this study, a data analytics approach was applied with the aim of determining relevant variables affecting snap occurrence. To validate this approach, a case study was conducted based on real-world data collected from an electrical steel reversing mill. The results suggested a selection of variables to characterize the quality issue of strip snap in the cold rolling process. This quality characterization study was performed as the preliminary stage of a quality improvement task.
Original language | English |
---|---|
Pages (from-to) | 453-458 |
Number of pages | 6 |
Journal | Procedia CIRP |
Volume | 81 |
DOIs | |
Publication status | Published - 20 Jun 2019 |
Externally published | Yes |
Event | 52nd CIRP Conference on Manufacturing Systems, CMS 2019 - Ljubljana, Slovenia Duration: 12 Jun 2019 → 14 Jun 2019 |
Keywords
- Cold rolling
- Data analytics
- Machine learning
- Quality improvement
- Strip breakage