Finite Element Simulation for Reducing Stress Concentration Around a Central Hole Using Optimized Adjacent Hole Designs
Bibliographic record
Abstract
A discontinuity and higher stress are experienced at the hole's edge when a circular hole is placed into a rectangular composite plate.The component will fail where the concentration of stress is highest.A cost-effective and lightweight solution is to create additional adjacent holes and use numerical methods and simulations to determine their positions and diameters.The objective of this study is to find the ideal locations, sizes, and forms of auxiliary slots in steel (AISI 4130) and aluminum (AA7075-T6) sheets using numerical techniques.SolidWorks was used to construct the models, and ANSYS was used to analyze the stress and deformation under different loads.The goal of the study is to improve the mechanical performance of the sheets and reduce the accumulation of stress near the central slot.In order to strengthen structural integrity, expand safety margins, and lessen stress concentrations, it also looks at the symmetrical distribution of slots in relation to existing slots.This improves the sheet's longevity under a variety of loading scenarios.The results showed that geometric adjustments to the hole distribution significantly improved the mechanical performance of both AISI 4130 steel and AA7075-T6 aluminum alloy.Both metals showed a progressive decline in ultimate stress values, indicating that the symmetrical hole design increases load transfer and reduces stress concentration.Aluminum shown a higher sensitivity to geometric adjustments, with a stress reduction of up to 22.5% compared to 18.6% for steel.Steel, on the other hand, demonstrated less stress dispersion and greater mechanical stability due to its strength and resistance to deformation.Statistical analysis revealed significant differences between the models, with the better design increasing structural efficiency by almost 25%.For applications requiring stiffness and long-term stability, steel is therefore considered more reliable; nonetheless, aluminum permits more design flexibility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".