Assistenzsystem zur Unterstützung der datengetriebenen Produktentwicklung

Description

Digital transformation, along with globalization, is a major challenge for companies today. In this change, engineers are challenged with new requirements on technical products and constantly increasing product complexity. Despite the diverse methodologies and tools available to engineers, it is often not possible to identify the root causes for deviating product properties, which prevents or at least makes it more difficult for virtual assurance of product properties in product development. In recent years, methods from the field of machine learning have found their way into product development. Machine learning methods enable the identification of correlations and trends in large data sets. On the one hand, however, product developers lack the methodological competence to develop and conduct analyses using machine learning methods. On the other hand, knowledge is required for data preparation and contextualization of the identified patterns. In this work, a concept for a knowledge‐based engineering assistance system to support data‐driven product development and its implementation is presented. By utilizing machine learning methods and their integration into existing product development processes, the potential of data‐driven analyses in the context of product properties assurance is revealed.