Collaborative Robotic Finishing Platform for Metal Part Processing Towards Industry 5.0
Bibliographic record
Abstract
Manual finishing operations in aerospace and ground transportation industries are often associated with health-and-safety-related issues such as musculoskeletal disorders, productivity loss, and challenges in workforce renewal. This work presents an innovative automated solution to address these challenges, prioritizing the ease of implementation and affordability for small and midsize enterprises (SMEs). Our proposed solution is a collaborative robotic (cobotic) finishing platform designed to eliminate labor-intensive work while keeping human operators in the loop to manage unforeseen situations. This platform aims to eliminate health risks, enhance repeatability, improve product quality, and increase productivity. This paper describes the mechanical design of the platform, its embedded cyber-physical system (CPS), interactivity features, as well as its risk assessment and risk mitigation. The platform integrates a UR10 cobot mounted upside down on a gantry structure to expand workspace, along with 3D sensors, scene cameras, compliance end-effectors, a dust collection system, programmable logic controllers (PLCs), and augmented reality projectors to assist the operator for easy execution of finishing tasks. The CPS comprises interconnected physical twins, their models, and relevant packages for the control of the whole finishing process from PLC to autonomous robot programming. Safety measures, including safety-rated devices, attenuation measures, and personal protective equipment, are integrated to ensure operator’s safety. While many research streams are fully integrated into the platform and CPS, some are still in the process of integration. Despite this, various finishing tasks have been successfully executed using the platform, demonstrating its potential to transform metal part finishing processes in industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".