| Abstract Scope |
Additive manufacturing is entering an era where artificial intelligence is increasingly used to accelerate materials discovery, process optimization, and in-situ monitoring. While these tools offer meaningful opportunities, they also raise fundamental questions about qualification, reproducibility, and trust. This keynote examines what the integration of AI genuinely changes in qualification workflows and, equally important, what it does not. Drawing on industrial, regulatory, and standards development experience, the talk argues that AI can enhance evidence generation, reduce experimental burden, and improve process understanding, but it does not replace the need for robust qualification frameworks, traceable data, or governance grounded in standards. The presentation will highlight emerging challenges related to data quality, model validation, and interoperability, and propose practical pathways for aligning AI-enabled manufacturing with existing and evolving qualification and standardization practices across additive manufacturing and beyond. |