Investigation of laser consolidation process for manufacturing structural components for advanced robotic mechatronics system (ARMS)
Bibliographic record
Abstract
Laser consolidation (LC) is a computer-aided manufacturing process that builds a complete part or features on an existing part, based on a CAD model, through the melting of injected metallic powder by a laser beam without the use of moulds or dies. Advanced Robotic Mechatronics System (ARMS) project was initiated by MD Robotics and supported by the Canadian Space Agency’s Space Technology Development Program. The main objective of this project was to investigate the use of enabling and emerging technologies for the design and manufacture of the next generation space robotic arms. The laser consolidation process was evaluated as a rapid functional prototype manufacturing process for the production of structural components using Ti-6Al-4V alloy. The LC Ti-6Al-4V components are metallurgically sound and show excellent mechanical properties. The LC process is particularly suitable for the manufacturing of components with complicated internal features or complex components consisting of multiple parts requiring to be integrated into one final assembly that are often very difficult or even impossible to produce using conventional manufacturing methods. The ARMS prototype robotic joint has been successfully assembled for demonstration at MDR and all structural components were manufactured from LC Ti-6Al-4V. In addition, the LC process also proved to be an excellent tool for the repair of parts damaged during the machining operations. The paper also discusses potential benefits and application of LC technology for in-space manufacturing.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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 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".