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Draft:Industrial large knowledge model

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Industrial large knowledge model (ILKM) is a conceptual framework proposed in the context of Industry 4.0 and smart manufacturing for integrating industrial data, domain knowledge, and artificial intelligence methods into a unified large language model supported knowledge-centric system. It aims to address limitations of large language models that are trained primarily on general knowledge and therefore lack industrial domain specificity. ILKM is intended to support domain-driven and data-centric industrial systems by organizing heterogeneous industrial information and enabling informed decision-making across industrial applications.

In the ILKM framework, industrial knowledge is constructed through a large knowledge library that includes both human-interpretable data, such as documents and multimedia content, and structured machine-generated data, such as sensor measurements and operational parameters. Domain-specific instruction data are used to guide the development of domain knowledge large language models, which function as expert components within industrial artificial intelligence systems. These components are designed to support tasks such as predictive maintenance, process optimization, quality control, engineering design, data analytics, and industrial question answering.

The concept of ILKM is closely related to research on industrial artificial intelligence, digital twins, and smart manufacturing systems. The framework emphasizes domain specificity, data privacy, and alignment with industrial constraints, and is guided by a set of development principles known as the “6S Principle,” which addresses specialized domain knowledge, scrutability, safety, scalability, sustainability, and systematization. As an emerging concept, ILKM remains an active area of research and discussion within the industrial AI community.

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  1. ↑ Lee, J., & Su, H. (2024). A unified industrial large knowledge model framework in Industry 4.0 and smart manufacturing. International Journal of AI for Materials and Design, 1(2), 41-47. https://accscience.com/journal/IJAMD/1/2/10.36922/ijamd.3681