Industrial Foundation Models (IFMs) for intelligent manufacturing: A systematic review

The remarkable success of Large Foundation Models (LFMs) has demonstrated their tremendous potential for manufacturing and sparked significant interest in the exploration of Industrial Foundation Models (IFMs). This study provides a comprehensive review of the current state of IFMs and their applications in intelligent manufacturing. It conducts an in-depth analysis from three perspectives, including data level, model level, and application level. The definition and framework of IFMs are discussed with a comparison to LFMs across these three perspectives. In addition, this paper provides a brief overview of the advancements in IFMs development across different countries, institutions, and regions. It explores the current application of IFMs, including Industrial Domain Models and Industrial Task Models, which are specifically designed for various industrial domains and tasks. Furthermore, key technologies critical to the training of IFMs are explored, such as data pre-processing, model fine-tuning, prompt engineering, and retrieval-augmented generation. This paper also highlights the essential capabilities of IFMs and their typical applications throughout the manufacturing lifecycle. Finally, it discusses the current challenges and outlines potential future research directions. This study aims to inspire new ideas for advancing IFMs and accelerating the evolution of intelligent manufacturing.