Maneuverability‐Based Speed and Temperature Adaptive Robotic Control (M‐ <scp>STARC</scp> ) for Fiber Steering in Additive Manufacturing
Bibliographic record
Abstract
ABSTRACT Steering of continuous fiber along three‐dimensional (3D) paths in automated fiber placement (AFP) additive manufacturing using a 6‐axis robotic arm requires advanced toolpath planning strategies to ensure coordinated control of robotic movements, printing speed, and deposition temperature. Fiber steering requires large nozzle rotations to keep the fibers tangential to the nozzle path. If the print speed is not reduced accordingly, the resulting large robot joint accelerations cause jerky movements and vibrations that disrupt the precise printing height—typically ranging from 0.1 to 0.3 mm—causing fiber damage at the nozzle tip and path errors. This research introduces a novel approach called Maneuverability‐based Speed and Temperature Adaptive Robotic Control (M‐STARC). The method dynamically adjusts printing speed and deposition temperature based on the complexity of the robotic joints' maneuvering required to maintain tangential alignment of the 3D printing nozzle with the fiber path trajectory. Heat transfer analyses determine nozzle temperature as a function of printing speed. This speed is varied along the trajectory to limit robot joint accelerations, which depend on the maneuverability (kinematics) of the robot. Faster printing speeds (and higher nozzle temperatures) are allowed at points where less maneuvering is needed. The proposed toolpath planning approach effectively defines the 3D path and robotic movements while adhering to critical speed–temperature constraints, laying the theoretical foundation for future experimental validation and implementation in fiber steering applications.
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 | 0.000 | 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) | 0.001 | 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".