Analysis and Optimization of Robotized Grinding of Titanium High Pressure Compressor Blades
Bibliographic record
Abstract
An analysis for a robotized grinding process of aerospace Titanium high pressure compressor blades was performed. In this process, the blade was grabbed on the robotic arm. A Scotch-Brite grinding wheel, on a pneumatic actuator, was used to grind the edges of the blades. The objective of this research work was to identify the major factors that influence the accuracy of the process and the final part quality. This objective was achieved by analyzing the dynamic characteristics of the wheel grabbed on the motor as well as analyzing the dynamic characteristics of the blade grabbed on the robotic arm. The frequency response functions (FRF) were identified at different robot configurations and positions. In addition, the vibrations of the various system components during the grinding process were monitored and analyzed to determine the effect of the speed on the relative vibrations between the workpiece and the wheel. Considering the dynamics of the wheel and the motor, rotational speed ranges were recommended. It was found that the vibrations of the grinding process were higher at two ranges: The first corresponds to the first natural frequency of the robot and the second corresponds to the first natural frequency of the wheel and the second natural frequency of the robot. By avoiding these ranges, part quality within the specified tolerances was obtained.
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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".