Belt grinding process with force control system for blade of aero-engine
Bibliographic record
Abstract
In abrasive belt grinding process for surface of blade, elasticity, deformation and abrasive belt wear are major factors affecting the machining stability, efficiency and quality. In order to improve the grinding process and realize optimal grinding, a new controllable and flexible belt grinding mechanism is designed and assembled in a special computer numerical control grinding machine, accompanied with a constant grinding force control system. Based on the analysis of proportional valve and cylinder system in this grinding mechanism, a mathematic model of normal grinding force is constructed. Afterward, a fuzzy proportional–integral–derivative control strategy is proposed to deal with the uncertainty and nonlinearity of this system. A Simulink model of force control process is developed, and the good performance is achieved according to the simulation result. Finally, several grinding experiments for an aero-engine fan blade are carried out. The measurements show that the proposed grinding process with fuzzy proportional–integral–derivative force controller enhances the machining stability and efficiency considerably. What is more, the machining qualities, such as surface roughness, form accuracy and consistency, are improved significantly. And all the grinding results satisfy the machining requirements in the manufacturing process of blade.
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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 | 0.000 |
| Research integrity | 0.000 | 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".