Video Diffusion based Digital Twin for Large Format Additive Manufacturing

Large Format Additive Manufacturing (LFAM) enables the fabrication of large, complex structures but presents challenges in thermal management, particularly in determining the optimal layer time to ensure interlayer bonding and structural integrity. Digital Twin (DT) technology has emerged as a key solution for predicting temperature distributions and optimizing process parameters. However, existing Physics-Based and Data-Driven DT models provide static, one-time predictions, lacking the adaptability to dynamically update thermal profile predictions based on real-time parameter adjustments. To address this limitation, we propose an adaptive Digital Twin framework based on the Video Diffusion Transformer (VDT). Unlike traditional DT models, our approach leverages Generative AI to dynamically simulate future temperature distributions when layer time or other printing parameters change. This method ensures that adjustments in printing strategy are immediately reflected in updated temperature predictions, leading to enhanced efficiency, improved print quality, and greater adaptability in LFAM workflows. Experimental results demonstrate that our approach is highly effective, generating realistic future frames that accurately reflect the temperature distribution. This work represents a significant step forward in Digital Twin technology, highlighting the potential of Generative AI in manufacturing.