FEATURE MINING OF MOULD LEVEL FLUCTUATION RELATED TO SLAG INCLUSIONS

This study investigates the correlation between mould level fluctuation and slag inclusion defects to trace defect origins. Fluctuation signals were processed with piecewise aggregate approximation (PAA) and low-pass filtering for smoothing and denoising. 853 time-frequency domain features were extracted. The Kolmogorov-Smirnov test and Fisher's exact test analyzed feature-defect correlations, with Benjamini-Yekutieli method for feature selection. A weighted random forest model evaluated feature mining methods. Experimental results demonstrate that the mined-feature model outperforms 1D-CNN using raw signals with enhanced interpretability. Distribution diagrams enable quantitative analysis of slag inclusion causes, supporting optimized mould level control strategies.