Applying Large Language Models for Intelligent Industrial Automation

From Theory to Application: Towards Autonomous Systems with Large Language Models

Abstract

This paper explores the transformative potential of Large Language Models (LLMs) in industrial automation, presenting a comprehensive framework for their integration into complex industrial systems. We begin with a theoretical overview of LLMs, elucidating their pivotal capabilities such as interpretation, task automation, and autonomous agent functionality. A generic methodology for integrating LLMs into industrial applications is outlined, explaining how to apply LLM for task-specific applications. Four case studies demonstrate the practical use of LLMs across different industrial environments: transforming unstructured data into structured data as asset administration shell model, improving user interactions with document databases through conversational systems, planning and controlling industrial operations autonomously, and interacting with simulation models to determine the parametrization of the process. The studies illustrate the ability of LLMs to manage versatile tasks and interface with digital twins and automation systems, indicating that efficiency and productivity improvements can be achieved by strategically deploying LLM technologies in industrial settings.