Developing a CAD-Based Digital Twin for Simulating Human-Robot Collaborative Disassembly of Complex Aerospace Components
Bibliographic record
Abstract
The effective simulation and optimization of human-robot collaborative (HRC) disassembly require detailed and structured digital models of end-of-life products. This research presents a practical methodology for developing a high-fidelity digital twin for this purpose, using an Avro Canada Orenda 10 turbojet engine combustion chamber as a case study. We detail the process of creating a comprehensive CAD model incorporating 32 interconnected components and their specific geometric and physical properties. This model serves as the geometric foundation for spatial analysis and for quantifying key task parameters, such as operational difficulty, which is derived from component mass and volume. Furthermore, we demonstrate the transformation of the CAD assembly’s structural dependencies into a graph-based representation, which provides the primary input for a reinforcement learning (RL) planning algorithm. This work showcases the critical link between detailed engineering design and AI-driven manufacturing, providing a replicable framework for creating virtual testbeds to validate advanced disassembly planning 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".