LLM-Powered Operator Advisor for Human-Robot Collaboration in Sustainable Assembly-Disassembly Cells

In response to the manufacturing shift toward customization and human-centered approaches in Industry 5.0, this paper presents a conceptual framework for a virtual operator advisor based on the Large Language Model (LLM). The LLM-powered advisor processes real-time data from the assembly or disassembly line, classifying it into operational efficiency, ergonomics, and sustainability metrics to deliver context-aware, adaptive guidance tailored to each task. It is achieved by cross-referencing these data with domain knowledge stored in the database and LLM capabilities. The Advisor recommends actions toward operations optimization, potential cognitive and physical strains, and environmental responsibility. As a framework that promotes workflow adaptability, sustainability, and worker well-being, it can contribute to the evolution of human-robot collaboration in modern manufacturing.

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Publication Details

Published
2025-05-26
DOI
https://doi.org/10.1109/ichms65439.2025.11154334
Citations
1
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
1.24
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article

LLM-Powered Operator Advisor for Human-Robot Collaboration in Sustainable Assembly-Disassembly Cells

Mahboobe Kheirabadi, Samira Keivanpour, Elham Ghorbani
1 citations
Manufacturing Process and Optimization
1.24
article

LLM-Powered Operator Advisor for Human-Robot Collaboration in Sustainable Assembly-Disassembly Cells

Mahboobe Kheirabadi, Samira Keivanpour, Elham Ghorbani
article en
1 citations

Abstract

In response to the manufacturing shift toward customization and human-centered approaches in Industry 5.0, this paper presents a conceptual framework for a virtual operator advisor based on the Large Language Model (LLM). The LLM-powered advisor processes real-time data from the assembly or disassembly line, classifying it into operational efficiency, ergonomics, and sustainability metrics to deliver context-aware, adaptive guidance tailored to each task. It is achieved by cross-referencing these data with domain knowledge stored in the database and LLM capabilities. The Advisor recommends actions toward operations optimization, potential cognitive and physical strains, and environmental responsibility. As a framework that promotes workflow adaptability, sustainability, and worker well-being, it can contribute to the evolution of human-robot collaboration in modern manufacturing.

Polytechnique Montréal (CA)
Openalex Percentile: Top 15%
Manufacturing Process and Optimization
1.24
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