| Abstract Scope |
The concept of Industry 4.0 or smart manufacturing is trying to integrate the automatization pyramid vertically. In practice the metal industry is characterized by the fact, that process control and administration of the plant management are only inadequately implemented. Although machine data and process data are collected, they are not widely used for further process automation and optimization. The metallurgical process control is mainly dependent on the experience of the employees. Data acquisition, data aggregation, data visualization, alarms and notifications, integration of enterprise systems, forms, dashboards and data analysis require a platform or intelligent industrial assistance systems with direct access to all production sites. With the help of these assistance systems a controllable process complexity can be managed without compromising process performance and robustness. Due to the complexity of the metallurgical processes themselves and the variability of the input materials, it is urgently necessary to support the process with modern software solutions for process automation. Our approach is to deal with the possibility of digitizing and optimizing the process route, analyzing this data and simulating it using a combination of machine learning approaches and conventional process simulation. We are closing this gap with our concept called DigMet 1.0, a tailormade software for process control, automatization and optimization for the whole process route. |