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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".