AI-Driven Large Language Models for Real-Time Safety Assessment in Human-Cobot Disassembly
Bibliographic record
Abstract
Disassembling end-of-life (EoL) products is essential for sustainable manufacturing but introduces safety challenges in human-robot collaboration (HRC). Conventional risk assessment (RA) methods-such as fault tree analysis (FTA), failure modes and effects analysis (FMEA), and hazard and operability studies (HAZOP)-lack the adaptability needed for real-time decision-making in dynamic disassembly environments. To address these limitations, we systematically review 14 studies from 2015 to 2025 and propose a validated AI-enhanced RA framework centered on large language models (LLMs). This framework integrates multi-modal data sources and safety standards (e.g., ISO/TS 15066) to enable real-time hazard detection, predictive analytics, and adaptive interventions. It is supported by prior simulation-based validation and structured evaluation metrics. The results offer a foundational approach to augmenting traditional RA techniques and advancing safe, human-centered cobotic disassembly systems.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".