Digital twins can significantly enhance efficiency and quality in additive manufacturing. By systematically capturing and analyzing all relevant production data, they provide a comprehensive understanding of the entire manufacturing process. This includes the integration of sensors and the manual entry of non-automatable data to create a complete dataset.
A key advantage of the digital twin is the continuous and automated optimization of processes. Through data- and AI-driven simulations, end-users can make precise predictions and identify potential sources of error at an early stage. This leads to a significant increase in the stability and accuracy of the additively manufactured components.
In the BigDataLMD project, Fraunhofer IAPT is developing a data infrastructure with SINUMERIK Edge to capture machine parameters as well as process-related data through in-situ sensors. A product digital twin is then created in Siemens Insights Hub® to analyze data and identify critical influencing factors. From this, recommendations for action are derived to improve the stability of the powder-laser-DED process.
