An integrated approach for power transformer modeling and manufacturing

Essential characteristics of smart factories, such as flexibility and resource efficiency, can be leveraged and improved by the power of machine learning and optimization techniques. For instance, the manufacturing process of a power transformer core constitutes a highly complex optimization problem. It involves creating a cost optimal slitting plan that meets all customer requirements and at the same time takes into account flexible and short-term constraints from production (e.g. current available metal bands in stock). As many of these constraints rely on forecasts, a learning system may provide the necessary predictions for these constraints. In addition, companies apply and maintain engineering software for a variety of tasks in construction, simulation, and interpretation of data. For instance, electrical engineers use a variety of tools to design an initial model of a power transformer according to customer requirements and constraints. Such tools often incorporate knowledge that serves as input for optimization and forecast models as described before. If these models are improved over time using external machine learning libraries, the newly developed models must find their way back into the implementation of engineering tools. Knowledge scattered across multiple software systems bears risk of being inconsistent. Furthermore, keeping knowledge consistent without a systematic approach is time-consuming and errorprone. In this paper, we describe an approach that leverages software engineering methods and tools and that supports knowledge transfer between software systems for optimization and modelling tasks. The approach follows the idea of a single source of knowledge together with transformation into different representations, as required by different engineering tasks. The proposed approach was introduced at an industrial site to improve the manufacturing process of power transformer cores.