Special Issue on Generative AI for Design, Manufacturing Processes, and Materials Systems: Part I

Abstract This guest editorial introduces Part I of the Special Issue on Generative AI for Design, Manufacturing Processes, and Materials Systems. By moving beyond traditional predictive modeling, generative AI enables end-to-end design creation, complex system optimization, and the synthesis of multimodal insights. Despite its promise, the rapid adoption of generative AI introduces critical challenges regarding design reliability, data fusion, and model adaptation in specialized, data-scarce engineering environments. To address these challenges, this issue compiles ten cutting-edge research papers organized into three central themes. The first theme focuses on leveraging LLMs for engineering design and knowledge retrieval, highlighting innovations in material selection, complex document comprehension, and electronic hardware reuse. The second theme explores generative and surrogate modeling, demonstrating how these frameworks aid in high-dimensional design optimization and multimodal data fusion in manufacturing. The final theme examines generative models in smart manufacturing, emphasizing robotic task planning, human-machine collaboration, and temporal data anomaly detection. Collectively, these contributions establish a robust foundation for future AI-driven engineering innovations.