| Abstract Scope |
Additive manufacturing (AM) is becoming increasingly data-driven and model-based, enabling more reliable processes and smarter approaches to part quality management. This presentation offers a comprehensive overview of the AM big data landscape, introduces key data fusion techniques, and highlights digital twin-enabled applications that are reshaping the industry.
First, core concepts and practical methodologies for multi-scale and multi-level data fusion are demonstrated using experiments from the NIST AM Metrology Testbed. These examples show how integrating diverse data streams enhances process monitoring, control, and part qualification.
After that, the presentation further explores holistic data fusion by combining cross-process measurements data, and simulation outputs to power AM digital twin technology that further enhances predictive quality management and adaptive process control. Finally, Actionable insights are provided on leveraging big data and multi-scale data fusion to advance intelligent quality assurance in additive manufacturing, with a forward-looking emphasis on the development of future standards. |