Constrained Trajectory Generation and Control for a 9-Axis Micromachining Center With Four Redundant Axes
Bibliographic record
Abstract
A control strategy and trajectory generation algorithm for a novel 9-axis micromachining center is presented. The micromachining center consists of a 3-axis gantry type micromill and a six degree of freedom magnetic rotary table. The micromill is modeled as a rigid body and controlled with a sliding mode controller and feedfoward friction compensator. The rotary table is modeled as a rigid body with flexible connections and controlled using a combination of notch filters, loop shaping controllers, and integrators. To improve the performance of the micromill, the tracking error of the micromill is sent as reference commands to the rotary table. The trajectory generation algorithm consists of a kinematic module and a feedrate optimizer. The kinematic module resolves the redundancies of the 9-axis micromachine while respecting the stroke limits and avoids singularities. The generated position commands by the kinematic module are optimized without violating the physical limits of the drives. A two dimensional contouring experiment has been carried out to validate the improved tracking error performance of the proposed strategy. A freeform surface has been machined to demonstrate the overall performance of the 9-axis machine tool.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".