Abstract
Recently, remarkable advances have been made in statistical analyses based on deep-learning techniques. Applied studies of deep learning have been reported in various industrial fields, including the iron and steel-making industries. The production of iron and steel requires a variety of processes, such as the processing of ingredients, iron-making, casting, and rolling. Consequently, the data acquired from them are diverse, and various tasks exist that can be assisted by deep-learning algorithms. Hence, providing a summary of the application is helpful for researchers specializing in information science to grasp the current trend of applied studies on deep learning techniques and for researchers specializing in each field of the iron and steel-making industry to understand what types of deep learning techniques are being utilized in other specialized fields. Therefore, in this study, we summarize the current studies on the application of deep learning in the iron- and steel-making fields by organizing them into several categories of processes and analytical methodologies. Furthermore, based on the results, we discuss future perspectives on the development of deep-learning techniques in this field.
