Leveraging Digital Twins to Optimize Additive Manufacturing Processes
The Industry 4.0 initiative has paved the way for extensive digitalization and virtualization in manufacturing processes. In this landscape, the digital twin— a virtual representation of a system, process, or product — can greatly improve both efficiency and product quality. By systematically gathering and analyzing all relevant production data, digital twin models offer a thorough understanding of the entire manufacturing workflow.
In the industry-funded ‘BigDataLMD’ project, Fraunhofer IAPT has created a data-pipeline infrastructure and accompanying software tools to facilitate the digitalization of a powder-based laser metal deposition system. Utilizing SINUMERIK Edge and in-situ optical sensors, the pipeline captures machine parameters and process-related data. A product digital twin is then generated in Siemens Insights Hub® for data analysis and identification of key influencing factors. Additionally, the software tool enables users to gather metadata from subsequent quality measurement steps. From the analyzed data, actionable recommendations are developed to enhance the stability of the powder-laser-DED process.
One of the primary benefits of the digital twin is the ongoing and automated optimization of processes. Through data- and AI-driven simulations, end-users can make accurate predictions and detect potential errors early, thereby enhancing the stability and precision of additively manufactured components.