Surface defects in steel slabs during continuous casting pose significant challengesfor quality control and product integrity in the steel industry. Predicting and classifyingthese defects accurately is crucial for ensuring product quality and minimizing productionlosses. This thesis investigates the effectiveness of machine learning models in predictingsurface defects of varying severity levels (ordinal classes) during the primary coolingstage of continuous casting. The study evaluates four machine learning algorithms,namely, XGBoost (main and baseline models), Decision Tree, and One-vs.-Rest SupportVector Machine (O-SVM), all trained with imbalanced defect class data. Model evaluationis conducted using a set of performance metrics, including precision, recall, F1-score,accuracy, macro-averaged Mean Absolute Error (MAE), Receiver Operating Characteristic(ROC) curves, Weighted Kappa and Ordinal Classification Index (OCI). Results indicatethat the XGBoost main model demonstrates robust performance across most evaluationmetrics, with high accuracy, precision, recall, and F1-score. Furthermore, incorporatingtemperature data from the primary cooling process inside the mold significantly enhancesthe predictive capabilities of machine learning models for defect prediction in continuouscasting. Key process parameters associated with defect formation, such as tundish temperature,casting speed, stopper rod argon pressure, and submerged entry nozzle (SEN) argonflow, are identified as significant contributors to defect severity. Feature importance andSHAP (SHapley Additive exPlanations) analysis reveal insights into the relationship betweenprocess variables and defect formation. Challenges and trade-offs, including modelcomplexity, interpretability, and computational efficiency, are discussed. Future researchdirections include further optimization and refinement of machine learning models andcollaboration with industry stakeholders to develop tailored solutions for defect predictionand quality control in continuous casting processes.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-205070 |
| Date | January 2024 |
| Creators | Sibanda, Jacob |
| Publisher | Linköpings universitet, Statistik och maskininlärning |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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