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Related Experiment Video

Updated: May 8, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

LLM-driven human-AI collaborative decision support system for complex industrial processes: A case study in

Youcheng Zong¹, Runda Jia¹, Kang Li²

¹College of Information Science and Engineering, Northeastern University, Shenyang, 110004, Liaoning, China.

Neural Networks : the Official Journal of the International Neural Network Society

|May 6, 2026

Summary

This summary is machine-generated.

This study introduces a human-AI system using Large Language Models (LLMs) for industrial decision support. It integrates diverse data, enhancing cognitive efficiency and providing accurate, interpretable recommendations for complex processes.

Keywords:

Human-AI collaboration Industrial processes Knowledge retrieval Large language models Meta-cognitive reasoning

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Area of Science:

Industrial Process Optimization
Artificial Intelligence in Manufacturing
Data Integration and Decision Support

Background:

Complex industrial processes generate siloed, heterogeneous data, impeding integration and decision-making.
Existing systems struggle with cognitive load and real-time data access for human operators.

Purpose of the Study:

To develop a human-AI collaborative decision support system for complex industrial processes.
To leverage Large Language Models (LLMs) for enhanced data integration and cognitive efficiency.

Main Methods:

Implemented a meta-cognitive reasoning framework for task decomposition and tool orchestration.
Integrated feedback-based knowledge retrieval and a memory-augmented SQL constructor for natural language data access.
Developed a unified architecture coordinating technical documents, process data, and operator feedback under constraints.

Main Results:

Achieved 99.1% answer correctness on an expert-annotated evaluation corpus using DeepSeek-V3 LLM.
Demonstrated reliable and efficient decision support within practical inference-time constraints.
Successfully deployed in a steelmaking plant's ladle preheating process.

Conclusions:

The human-AI system effectively overcomes data silos and enhances decision-making in complex industrial settings.
LLM-driven systems offer interpretable recommendations while maintaining human control and safety.
The proposed architecture facilitates seamless integration of diverse data sources for real-time industrial applications.