Knowledge graphs for the enhancement of process planning in manufacturing

Abstract

The increasing complexity of modern manufacturing systems, driven by demands for sustainability, shorter product life cycles, and greater customization, necessitates frequent reconfiguration and redesign of production processes. In this context, knowledge about parameter interdependencies is crucial. Additionally, simulations play a vital role by enabling virtual testing of various parameter configurations to optimize key performance indicators such as energy consumption, emissions, processing time, and costs. This project report presents an integrated approach to enhance process planning by incorporating semantic models that describe process parameter interdependencies. Furthermore, we describe a concept for automating the generation of simulation sequences, thereby reducing the complexity and effort involved in manual planning. The combination of these approaches is demonstrated using a web-based application, showcasing the potential to support efficient and sustainable manufacturing practices.