Pre-Trained CNN for Classification of Time Series Images of Anti-Necking Control in a Hot Strip Mill

Abstract

The steel industry is highly competitive, and companies must make the most of their current resources to maximise efficiency and, therefore, profitability. In the age of Industry 4.0, data is one of the most valuable resources available and, with appropriate processing and analysis, can improve the quality and adaptability of various applications. One such application is the monitoring and classification of AntiNecking Control in a Hot Strip Mill. This paper proposes a deep learning approach to this application through the use of a pre-trained Convolutional Neural Network. The proposed system binarily classifies the timing of Anti-Necking Control strokes and has been optimised using grid search optimisation in conjunction with k-fold cross validation to determine an optimal time series image classification model.