Design, analysis and testing of a radial-axial hybrid active force compliant tool head for deburring turbine engine parts
Bibliographic record
Abstract
In this thesis, a new concept and design is presented for a tool with the purpose of deburring gas turbine engine parts. This new concept utilizes both axial and radial active force compliance to accomplish the burr removal in a more robust manner. The axial and radial components are integrated in a manner that allows them to be decoupled, reducing the complexity of the system. The tool is designed around a pneumatic spindle that is affixed to pneumatic axial actuators. The axial motion system is then affixed to the radial system which makes use of a 2 axis rotary gimbal, acting as a 2-D pivot. Sensors for the axial and radial components of the tool are independent of each other. Axial sensing is accomplished using a commercial string-potentiometer and radial sensing is accomplished using magnets and magnetic field sensors. Burr formation and methods of removal are discussed. Different deburring tool designs available commercially and through literature are then explored. The design process of selecting axial and radial actuation and sensing and integrating them together while keeping the systems decoupled is outlined. Modeling of the tool is then developed and a simulation of the tool is presented to illustrate the deburring mechanics of the decoupled axial and radial components. Experimentation to determine the stiffness qualities of the tool as well as calibration of the sensors are presented and used within the simulation.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".