The production of steel coils is constrained by accurately defined properties, like grade, surface, geometry and mechanical properties. These product characteristics are of critical importance throughout the manufacturing chain for which process parameters and installation settings have to be calibrated, adjusted and controlled. The manufacturing of steel coils is performed in several steps, from slabs at steel casting, towards steel coils by reduction in thickness and forming of mechanical properties via rolling mills. Further additional processing is done for surface treatment of the steel coils, like galvanizing, towards an end-product. This technique is being used to manufacture car doors, hoods, refrigerator doors, razor blades, etcetera. At each process step value is added to the main characteristics within tighter tolerances.
If the characteristics of the steel deviates from specifications, cracks may occur during the stamping process and surface defects may influence the quality of the coatings later in the process, downgrading the steel product. The process to resolve these problems and to “recalibrate” the rolling and coating installations is very costly in terms of time, fault products, production loss and loss of source material. Like any high-end industrial production process, steel coil production processes and automotive stamping processes typically generate huge volumes of high dimensional process control and product quality data, spread over several plants and process stages.
The ‘PROMIMOOC’ project aims at developing a generic platform for data collection and integration, data-driven modeling and model-based online process control, by which the steel production can be adapted and optimized in real-time. In contrast to more traditional approaches (e.g. six sigma), the project aims to combine distributed in-memory database technology, algorithms for nonlinear data mining and automatic model updates and nonlinear multiple objective decision making algorithms into a real-time system for process control. The approach allows for finding optimal process control compromises for conflicting goals such as e.g. product quality, process robustness, and production cost.
Online big data analytics: high-volume non-standardized data streams generated at different locations need to be combined with a large quantity of historical data to facilitate data mining. Data mining algorithms need to be integrated into a distributed in-memory database to achieve online big data analytics.
Automatic generation of models based on big data: the relationship between process control data and product properties will be modeled through state-of-the-art nonlinear regression algorithms (e.g., support vector machines, random forests, etcetera). The automatic generation, comparison, selection and update of such models will be a major task within the project.
Multiple objective decision making based on models: the data driven models will be combined with nonlinear global optimization algorithms for finding Pareto-optimal process control settings to achieve best compromises between the conflicting goals, under complex constraints. Evolutionary strategies will be used for this task.
