Collaborative Robotic Finishing Platform for Metal Part Processing Towards Industry 5.0
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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