Type de document
Études primaires
Année de publication
2026
Langue
Anglais
Titre de la revue
Robotics and Computer-Integrated Manufacturing
Résumé
Disassembly tasks in human–robot collaboration (HRC) environments present safety challenges due to hazardous materials, control system variability, and physically demanding operator tasks. To address these challenges, we propose an AI-augmented risk assessment framework integrating System-Theoretic Process Analysis (STPA) and Failure Mode and Effects Analysis (FMEA). This framework is implemented in four configurations: Term Frequency– Inverse Document Frequency (TF-IDF), Fine-tuned Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and RAG with a structured Knowledge Graph (KG) built from safety standards. The system supports real-time, standards-compliant safety reasoning by generating interpretable, context-specific recommendations. We evaluate these configurations across GPT-3.5 TURBO, GPT-4o, GPT-4.1, and open-source LLMs Qwen2.5 (3B) and Ministral (3B). Among all, RAG+KG with GPT-4.1 achieved the highest results across language-based metrics (BLEU: 68.3, ROUGE-L: 72.0, Semantic Similarity: 81.1, BERTScore (F1): 90.0) and safety-specific metrics (Hazard Recall: 92, Compliance Precision: 97, Safety Violation Rate: zero). Six safetyoriented metrics were introduced to assess compliance, hazard coverage, interpretability, and robustness. A case study on electrical vehicle (EV) battery module disassembly demonstrated the system’s effectiveness in identifying unsafe control actions, tracing failure modes, and recommending targeted mitigation strategies for mechanical, electrical, and chemical hazards, and ergonomic considerations. This framework offers a scalable, explainable approach to real-time safety analysis, advancing AI-enabled risk assessment in dynamic HRC disassembly tasks and supporting the vision of human-centered Industry 5.0 manufacturing.
Mots-clés
Grand modèle de langage, Large language model, Robot collaboratif, Collaborative robot, Montage et démontage, Assembly and disassembly, Évaluation du risque, Hazard evaluation
Numéro de projet IRSST
n/a
Citation recommandée
Alenjareghi, M. J., Ghorbani, F., Keivanpour, S., Chinniah, Y. A. et Jocelyn, S. (2026). Proactive safety reasoning in human-robot collaboration in disassembly through LLM-augmented STPA and FMEA. Robotics and Computer-Integrated Manufacturing, 98, article 103162. https://doi.org/10.1016/j.rcim.2025.103162
Included in
Industrial Engineering Commons, Occupational Health and Industrial Hygiene Commons, Risk Analysis Commons, Robotics Commons