Digital Twin for Automated Post-processing Chain in Additive Manufacturing

Digital twins offer manufacturers a valuable opportunity to optimize production processes, improve product quality, and reduce costs through techniques such as simulation, data analysis, and artificial intelligence using real-world data. Despite these advantages, the implementation of digital twins faces significant challenges, particularly in data integration within existing systems and processes. This paper outlines essential requirements for creating a digital twin for automated post-processing of additively manufactured components, emphasizing the modeling of post-processing contexts and integration of relevant data. Using a reference architecture, the study identifies technologies that facilitate the development and deployment of digital twins in manufacturing environments. Key findings underscore the critical need for a cohesive knowledge model that seamlessly integrates relationships and rules, while also identifying gaps in the ISO standard with respect to comprehensive knowledge modeling and gap analysis. Future research directions will include exploring scalability and integrating artificial intelligence to enhance process optimization, while also addressing the challenges associated with the availability of digital data.