Optimization of Precision Boring Conditions to Enhance the Surface Integrity of a Landing Gear Component
Bibliographic record
Abstract
Boring process, also referred to internal turning, is commonly used to machine critical features of landing gear components like struts, brackets, and main cylinders. Over the past years, extensive research efforts have addressed the stability of the process by developing instrumented boring bars and advanced monitoring techniques. However, although the surface integrity characteristics, particularly the residual stresses, are crucial for structural components, it hasn’t been considered and its evolution over the boring conditions still not well understood. Hence, the present paper proposes a comprehensive investigation on the effects of boring conditions on the surface integrity of the aluminum alloy 7175-T74 commonly used in landing gear components. A parametric analysis has shown that lower cutting forces and surface roughness can be achieved using a larger insert nose radius. It was also found that feed rate, cutting speed and depth of cut experienced strong interaction effects with the machining mode (dry/wet) regarding the resultant cutting force and surface roughness. Results have also shown that wet boring conditions generated compressive residual stresses. An optimal boring condition was obtained using Grey relational analysis (GRA) – Taguchi method. Further investigation is required to refine the obtained optimal machining condition by considering the GRA results and the parametric analysis outcomes.
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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) | 0.000 | 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".