CAM-Based Planning, Programming and Execution of Large-Scale Machining Operations by a Robot-Mounted Gantry System
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This paper examines issues related to planning, programming and execution of machining operations by a robot in the context of machining large parts with complex geometries by a gantry-mounted robotic system. Parts were created from surface data in a CAD/CAM environment. The same environment was used to generate tool paths using a conventional machine tool approach. These paths were converted to robot trajectories and validated using mathematical kinematic models of the robotic system. Validation was performed according to various criteria related to process performance. Associated robot programs were then automatically generated. The manufacturing cell was progressively integrated according to requirements resulting from iterative process characterization. A metrology-based calibration procedure was designed that considerably improved the system's positioning precision. A custom user interface for online process monitoring was created as well as online cell program management capabilities to accommodate the large quantity of trajectory information. The system went from conceptual, to prototype-level to full production mode, thereby covering several Manufacturing Readiness Levels (MRL). The various strategies used in planning the process and programming the system demonstrate the viability of CAM-based approach for robot machining path generation without resorting to the use of OLP packages for validation of program generation. This could create a shift in the dependency on robotic specialists, allowing machine tool experts to carry about the programming of robot machining cells using the conventional machine tool approach.</div></div>
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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".