Proactive safety reasoning in human-robot collaboration in disassembly through LLM-augmented STPA and FMEA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• RAG+KG framework enables real-time, standards-compliant safety analysis in HRC. • GPT-4.1 delivers the most accurate and compliant safety recommendations in tests. • STPA and FMEA integration supports both control-level and component-level risks. • EV battery case study confirms hazard detection and mitigation across risk types. • Six safety-oriented evaluation metrics introduced, achieving 92 % hazard recall. 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 safety-oriented 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. LLM-Augmented Risk Analysis in Human–Robot Disassembly: From Safety Challenges to Standards-Compliant Real-Time Recommendations
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it