| Abstract Scope |
Digital Twin, originally introduced by NASA in 2003, has gained increasing attention in additive manufacturing due to recent advances in sensing, computation, and data-driven modeling. This research presents a framework for developing a digital twin to improve and optimize additive manufacturing processes. By integrating virtual representation, real-time monitoring, predictive modeling, control strategies, and continuous optimization, the proposed framework aims to enhance process understanding while reducing experimental iterations, material waste, and development costs. This poster demonstrates how these key components are integrated into the additive manufacturing workflow and presents preliminary results from in-house system development. |