Industrial Foundation Model

Area of Science:

• Artificial Intelligence
• Industrial Engineering

Background:

• General foundation models exhibit powerful capabilities but struggle with specialized industrial data modalities, complex tasks, and trustworthy output requirements.
• The limitations of current models necessitate the development of Industrial Foundation Models (IFMs) tailored for industrial applications.

Purpose of the Study:

• To propose a system architecture, IFMsys, for the development and application of Industrial Foundation Models.
• To address the challenges of specialized data, complex tasks, and trustworthiness in industrial settings.

Main Methods:

• IFMsys architecture encompasses model training (pretraining on multimodal industrial data and fine-tuning with mechanisms), model adaptation (task-oriented and domain-specific fine-tuning), and model application (agent-centric collaboration and application framework).
• Development of a prototype IFM system named MetaIndux.

Main Results:

• The proposed IFMsys architecture provides a framework for creating specialized IFMs.
• The MetaIndux prototype demonstrates successful application in typical industrial tasks.
• The system enhances industrial product lifecycle applications through an agent-centric collaboration system.

Conclusions:

• The IFMsys architecture and MetaIndux prototype offer a viable solution for industrial foundation models.
• This research paves the way for advancements in IFM theories, technologies, and applications.
• Future research directions and open issues in the field of IFM are identified.