Hybrid-Input FCN-CNN-SE for Industrial Applications: Classification of Longitudinal Cracks during Continuous Casting
Davi Alberto Sala, Andy Van Yperen-De Deyne, Erik Mannens, Azarakhsh Jalalvand
Abstract
In the presented research, machine learning methods were applied to the prediction of longitudinal cracks in steel slabs during continuous casting. We employ a deep learning approach to process 68 thermocouple signals as a multivariate time series (MTS) along with 32 static features, which encompass both chemical composition and process information. Our deep learning approach integrates two distinct parallel modules, followed by an aggregation block; a Convolutional Neural Network (CNN) processes the thermocouple MTS, while in parallel, the static data undergo processing via a Fully Connected Network (FCN). To enhance the performance of the CNN, we incorporate two Squeeze and Excitation (SE) blocks, which act as an attention mechanism across different channels. By integrating chemical information with MTS in the detection system, we improve the performance of defect detection by 15% relatively.