Artificial Intelligence (AI) and Machine Learning (ML) are transforming additive manufacturing (AM) from a parameter-driven process into an adaptive, data-driven production system. This workshop focuses on the integration of AI across the AM value chain—design optimisation, print-path generation, real-time anomaly detection, predictive maintenance, and closed-loop process control—enabling the production of “born-qualified” parts.

The sub-workshop explores AI-enhanced metal powder bed fusion (PBF), directed energy deposition (DED), binder jetting, and polymer AM systems. Key topics include in-situ monitoring, sensor fusion, digital twins, data infrastructure, and explainable AI for certification environments. Presentations will address recent advances in deep learning for defect prediction, adaptive scan strategies, and automated quality assurance.

Speakers from academia will present state-of-the-art research in data-driven modelling and intelligent control systems, while industry leaders will share practical experience implementing AI solutions in serial production environments. Particular emphasis will be placed on qualification-by-design, reduced inspection costs, and accelerated certification pathways.

The workshop aims to provide a comprehensive understanding of how AI integration can enhance productivity, reliability, and scalability in additive manufacturing, supporting the transition toward autonomous, smart AM factories.

Extended Key Takeaways & Outputs

• Comparative overview of AI applications across major AM technologies

• Identification of bottlenecks in data quality, standardisation, and certification

• Strategies for implementing real-time monitoring and closed-loop control

• R&D priorities for achieving “born-qualified” additive parts

• Roadmap for AI-enabled smart AM factories and autonomous production systems