Utilizing LLMs for Industrial Process Automation
A growing number of publications address the best practices to use Large Language Models (LLMs) for software engineering in recent years. However, most of this work focuses on widely-used general purpose programming languages like Python due to their widespread usage training data. The utility of LLMs for software within the industrial process automation domain, with highly-specialized languages that are typically only used in proprietary contexts, remains underexplored. This research aims to utilize and integrate LLMs in the industrial development process, solving real-life programming tasks (e.g., generating a movement routine for a robotic arm) and accelerating the development cycles of manufacturing systems.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Utilizing LLMs for Industrial Process Automation: A Case Study on Modifying RAPID Programs
How to best use Large Language Models (LLMs) for software engineering is covered in many publications in recent years. However, most of this work focuses on widely-used general purpose programming languages. The utility …
Control Industrial Automation System with Large Language Model Agents
Traditional industrial automation systems require specialized expertise to operate and complex reprogramming to adapt to new processes. Large language models offer the intelligence to make them more flexible and easier t…
Language ModelingLanguage ModellingLarge Language ModelTowards autonomous system: flexible modular production system enhanced with large language model agents
In this paper, we present a novel framework that combines large language models (LLMs), digital twins and industrial automation system to enable intelligent planning and control of production processes. We retrofit the a…
DescriptiveLanguage ModelingLanguage ModellingLarge Language ModelDeep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning
In this article, the concepts of transfer and continual learning are introduced. The ensuing review reveals promising approaches for industrial deep transfer learning, utilizing methods of both classes of algorithms. In …
Continual LearningTransfer LearningIntegrating Large Language Model Agents with Digital Twins for Industrial Autonomous Systems
Industrial automation is being transformed by digitalization and the increasing use of cyber-physical systems. Modern production environments require greater adaptability, faster reconfiguration, and more intuitive human…