Comparison of Feature Selection Methods for Mechanical Properties of Cold Rolled Products in Flat Steel Manufacturing

The mechanical properties of steel are critical for ensuring its quality and are traditionally tested using destructive methods, which involve cutting test samples after the skin-rolling process. This procedure necessitates the scrapping of the last 8 meters of the coil and extracting a 500 mm wide sample, consuming approximately 1 to 1.5 minutes. To eliminate these additional process steps and minimize material waste, this study aims to predict steel coils' yield strength and tensile strength in the flat steel industry using six machine learning models. The models incorporate 24 distinct production parameters as inputs. The models examined include Linear Regression, Support Vector Regressor (SVR), Decision Tree, K-Nearest Neighbors (KNN), Random Forest, and eXtreme Gradient Boosting (XGBoost). To enhance the predictive performance of these models, seven different feature selection methods are employed. These methods systematically rank the production parameters based on their influence and are iteratively utilized within the models to refine their accuracy. The application of these feature selection techniques significantly improves the models' efficiency, leading to substantial operational benefits. The study demonstrates that machine learning models, when optimized with advanced feature selection methods, can accurately predict the mechanical properties of steel, thereby reducing the need for destructive testing. This approach not only conserves material and time but also enhances the overall efficiency of the production process in the flat steel industry.